In this paper, we aim to obtain the location information of a sensor node deployed in a Wireless Sensor Network (WSN). Here, Time of Arrival based localization technique is considered. We calculate the position information of an unknown sensor node using the non- linear techniques. The performances of the techniques are compared with the Cramer Rao Lower bound (CRLB). Non-linear Least Squares and the Maximum Likelihood are the non-linear techniques that have been used to estimate the position of the unknown sensor node. Each of these non-linear techniques are iterative approaches, namely, Newton
Raphson estimate, Gauss Newton Estimate and the Steepest Descent estimate for comparison. Based on the
results of the simulation, the approaches have been compared. From the simulation study, Localization
based on Maximum Likelihood approach is having higher localization accuracy.
On the approximation of the sum of lognormals by a log skew normal distributionIJCNCJournal
Several methods have been proposed to approximate the sum of lognormal RVs. However the accuracy of each method relies highly on the region of the resulting distribution being examined, and the individual lognormal parameters, i.e., mean and variance. There is no such method which can provide the needed accuracy for all cases. This paper propose a universal yet very simple approximation method for the sum of Lognormals based on log skew normal approximation. The main contribution on this work is to propose an analytical method for log skew normal parameters estimation. The proposed method provides highly accurate approximation to the sum of lognormal distributions over the whole range of dB spreads for any correlation coefficient. Simulation results show that our method outperforms all previously proposed methods and provides an accuracy within 0.01 dB for all cases.
On the approximation of the sum of lognormals by a log skew normal distributionIJCNCJournal
Several methods have been proposed to approximate the sum of lognormal RVs. However the accuracy of each method relies highly on the region of the resulting distribution being examined, and the individual lognormal parameters, i.e., mean and variance. There is no such method which can provide the needed accuracy for all cases. This paper propose a universal yet very simple approximation method for the sum of Lognormals based on log skew normal approximation. The main contribution on this work is to propose an analytical method for log skew normal parameters estimation. The proposed method provides highly accurate approximation to the sum of lognormal distributions over the whole range of dB spreads for any correlation coefficient. Simulation results show that our method outperforms all previously proposed methods and provides an accuracy within 0.01 dB for all cases.
Graphical Visualization of MAC Traces for Wireless Ad-hoc Networks Simulated ...idescitation
Many network simulators (e.g., ns2) are already
being used for performing wired and wireless network
simulations. But, with the current graphical visualization
support in-built in ns2, it is difficult to understand the node
status, packet status and the MAC level events particularly
for Ad-hoc networks. In this paper, we extend the visualization
support in ns-2 that should help research community in the
area of wireless networks to analyze different MAC level
events in an efficient manner. In particular, we have developed
two types of visualizations namely, temporal and spatial.
Temporal visualization helps to analyze success or failure of
a packet with respect to time while spatial visualization helps
to understand the effects due to proximity of nodes. The trace
is made highly configurable in terms of different attributes
like specific nodes and time duration.
Classification of Iris Data using Kernel Radial Basis Probabilistic Neural Ne...Scientific Review
Radial Basis Probabilistic Neural Network (RBPNN) has a broader generalized capability that been successfully applied to multiple fields. In this paper, the Euclidean distance of each data point in RBPNN is extended by calculating its kernel-induced distance instead of the conventional sum-of squares distance. The kernel function is a generalization of the distance metric that measures the distance between two data points as the data points are mapped into a high dimensional space. During the comparing of the four constructed classification models with Kernel RBPNN, Radial Basis Function networks, RBPNN and Back-Propagation networks as proposed, results showed that, model classification on Iris Data with Kernel RBPNN display an outstanding performance in this regard.
Enhancement and Analysis of Chaotic Image Encryption Algorithms cscpconf
The focus of this paper is to improve the level of security and secrecy provided by the chaotic
map based image encryption.An encryption algorithm based on the Logistic and the Henon
maps is proposed. The algorithm uses chaotic iteration to generate the encryption keys, and
then carries out the XOR and cyclic shift operations on the plain text to change the values of
image pixels. Chaotic Map Lattice based image encryption algorithm suggested by Pisarchik is
also examined which is based on Logistic map alone. In experiments, the corresponding results
showed the proposed method is a promising scheme for image encryption in terms of security
and secrecy. At the end, we show the results of a security analysis and a comparison of both
schemes
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
COMPARATIVE PERFORMANCE ANALYSIS OF RNSC AND MCL ALGORITHMS ON POWER-LAW DIST...acijjournal
Cluster analysis of graph related problems is an important issue now-a-day. Different types of graph
clustering techniques are appeared in the field but most of them are vulnerable in terms of effectiveness
and fragmentation of output in case of real-world applications in diverse systems. In this paper, we will
provide a comparative behavioural analysis of RNSC (Restricted Neighbourhood Search Clustering) and
MCL (Markov Clustering) algorithms on Power-Law Distribution graphs. RNSC is a graph clustering
technique using stochastic local search. RNSC algorithm tries to achieve optimal cost clustering by
assigning some cost functions to the set of clusterings of a graph. This algorithm was implemented by A.
D. King only for undirected and unweighted random graphs. Another popular graph clustering
algorithm MCL is based on stochastic flow simulation model for weighted graphs. There are plentiful
applications of power-law or scale-free graphs in nature and society. Scale-free topology is stochastic i.e.
nodes are connected in a random manner. Complex network topologies like World Wide Web, the web of
human sexual contacts, or the chemical network of a cell etc., are basically following power-law
distribution to represent different real-life systems. This paper uses real large-scale power-law
distribution graphs to conduct the performance analysis of RNSC behaviour compared with Markov
clustering (MCL) algorithm. Extensive experimental results on several synthetic and real power-law
distribution datasets reveal the effectiveness of our approach to comparative performance measure of
these algorithms on the basis of cost of clustering, cluster size, modularity index of clustering results and
normalized mutual information (NMI).
ENHANCEMENT OF TRANSMISSION RANGE ASSIGNMENT FOR CLUSTERED WIRELESS SENSOR NE...IJCNCJournal
Transmitter range assignment in clustered wireless networks is the bottleneck of the balance between
energy conservation and the connectivity to deliver data to the sink or gateway node. The aim of this
research is to optimize the energy consumption through reducing the transmission ranges of the nodes,
while maintaining high probability to have end-to-end connectivity to the network’s data sink. We modified
the approach given in [1] to achieve more than 25% power saving through reducing cluster head (CH)
transmission range of the backbone nodes in a multihop wireless sensor network with ensuring at least
95% end-to-end connectivity probability.
A New Approach for Speech Enhancement Based On Eigenvalue Spectral SubtractionCSCJournals
In this paper, a phase space reconstruction-based method is proposed for speech enhancement. The method embeds the noisy signal into a high dimensional reconstructed phase space and uses Spectral Subtraction idea. The advantages of the proposed method are fast performance, high SNR and good MOS. In order to evaluate the proposed method, ten signals of TIMIT database mixed with the white additive Gaussian noise and then the method was implemented. The efficiency of the proposed method was evaluated by using qualitative and quantitative criteria.
Information Content of Complex NetworksHector Zenil
This short talk given in Stockholm, Sweden, explains how algorithmic complexity measures, notably Kolmogorov complexity approximated both by lossless compression algorithms and the Block Decomposition Method (BDM) are capable of characterizing graphs and networks by some of their group-theoretic and topological properties, notably graph automorphism group size and clustering coefficients of complex networks. The method distinguished between models of networks such as regular, random, small-world and scale-free.
Digital camera and mobile document image acquisition are new trends arising in the world of Optical Character Recognition and text detection. In some cases, such process integrates many distortions and produces poorly scanned text or text-photo images and natural images, leading to an unreliable OCR digitization. In this paper, we present a novel nonparametric and unsupervised method to compensate for
undesirable document image distortions aiming to optimally improve OCR accuracy. Our approach relies on a very efficient stack of document image enhancing techniques to recover deformation of the entire document image. First, we propose a local brightness and contrast adjustment method to effectively handle lighting variations and the irregular distribution of image illumination. Second, we use an optimized greyscale conversion algorithm to transform our document image to greyscale level. Third, we sharpen the
useful information in the resulting greyscale image using Un-sharp Masking method. Finally, an optimal global binarization approach is used to prepare the final document image to OCR recognition. The proposed approach can significantly improve text detection rate and optical character recognition
accuracy. To demonstrate the efficiency of our approach, an exhaustive experimentation on a standard dataset is presented.
A novel method is proposed for image segmentation based on probabilistic field theory. This model assumes that the whole pixels of an image and some unknown parameters form a field. According to this model, the pixel labels are generated by a compound function of the field. The main novelty of this model is it consider the features of the pixels and the interdependent among the pixels. The parameters are generated by a novel spatially variant mixture model and estimated by expectation-maximization (EM)-
based algorithm. Thus, we simultaneously impose the spatial smoothness on the prior knowledge. Numerical experiments are presented where the proposed method and other mixture model-based methods were tested on synthetic and real world images. These experimental results demonstrate that our algorithm achieves competitive performance compared to other methods.
Graphical Visualization of MAC Traces for Wireless Ad-hoc Networks Simulated ...idescitation
Many network simulators (e.g., ns2) are already
being used for performing wired and wireless network
simulations. But, with the current graphical visualization
support in-built in ns2, it is difficult to understand the node
status, packet status and the MAC level events particularly
for Ad-hoc networks. In this paper, we extend the visualization
support in ns-2 that should help research community in the
area of wireless networks to analyze different MAC level
events in an efficient manner. In particular, we have developed
two types of visualizations namely, temporal and spatial.
Temporal visualization helps to analyze success or failure of
a packet with respect to time while spatial visualization helps
to understand the effects due to proximity of nodes. The trace
is made highly configurable in terms of different attributes
like specific nodes and time duration.
Classification of Iris Data using Kernel Radial Basis Probabilistic Neural Ne...Scientific Review
Radial Basis Probabilistic Neural Network (RBPNN) has a broader generalized capability that been successfully applied to multiple fields. In this paper, the Euclidean distance of each data point in RBPNN is extended by calculating its kernel-induced distance instead of the conventional sum-of squares distance. The kernel function is a generalization of the distance metric that measures the distance between two data points as the data points are mapped into a high dimensional space. During the comparing of the four constructed classification models with Kernel RBPNN, Radial Basis Function networks, RBPNN and Back-Propagation networks as proposed, results showed that, model classification on Iris Data with Kernel RBPNN display an outstanding performance in this regard.
Enhancement and Analysis of Chaotic Image Encryption Algorithms cscpconf
The focus of this paper is to improve the level of security and secrecy provided by the chaotic
map based image encryption.An encryption algorithm based on the Logistic and the Henon
maps is proposed. The algorithm uses chaotic iteration to generate the encryption keys, and
then carries out the XOR and cyclic shift operations on the plain text to change the values of
image pixels. Chaotic Map Lattice based image encryption algorithm suggested by Pisarchik is
also examined which is based on Logistic map alone. In experiments, the corresponding results
showed the proposed method is a promising scheme for image encryption in terms of security
and secrecy. At the end, we show the results of a security analysis and a comparison of both
schemes
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
COMPARATIVE PERFORMANCE ANALYSIS OF RNSC AND MCL ALGORITHMS ON POWER-LAW DIST...acijjournal
Cluster analysis of graph related problems is an important issue now-a-day. Different types of graph
clustering techniques are appeared in the field but most of them are vulnerable in terms of effectiveness
and fragmentation of output in case of real-world applications in diverse systems. In this paper, we will
provide a comparative behavioural analysis of RNSC (Restricted Neighbourhood Search Clustering) and
MCL (Markov Clustering) algorithms on Power-Law Distribution graphs. RNSC is a graph clustering
technique using stochastic local search. RNSC algorithm tries to achieve optimal cost clustering by
assigning some cost functions to the set of clusterings of a graph. This algorithm was implemented by A.
D. King only for undirected and unweighted random graphs. Another popular graph clustering
algorithm MCL is based on stochastic flow simulation model for weighted graphs. There are plentiful
applications of power-law or scale-free graphs in nature and society. Scale-free topology is stochastic i.e.
nodes are connected in a random manner. Complex network topologies like World Wide Web, the web of
human sexual contacts, or the chemical network of a cell etc., are basically following power-law
distribution to represent different real-life systems. This paper uses real large-scale power-law
distribution graphs to conduct the performance analysis of RNSC behaviour compared with Markov
clustering (MCL) algorithm. Extensive experimental results on several synthetic and real power-law
distribution datasets reveal the effectiveness of our approach to comparative performance measure of
these algorithms on the basis of cost of clustering, cluster size, modularity index of clustering results and
normalized mutual information (NMI).
ENHANCEMENT OF TRANSMISSION RANGE ASSIGNMENT FOR CLUSTERED WIRELESS SENSOR NE...IJCNCJournal
Transmitter range assignment in clustered wireless networks is the bottleneck of the balance between
energy conservation and the connectivity to deliver data to the sink or gateway node. The aim of this
research is to optimize the energy consumption through reducing the transmission ranges of the nodes,
while maintaining high probability to have end-to-end connectivity to the network’s data sink. We modified
the approach given in [1] to achieve more than 25% power saving through reducing cluster head (CH)
transmission range of the backbone nodes in a multihop wireless sensor network with ensuring at least
95% end-to-end connectivity probability.
A New Approach for Speech Enhancement Based On Eigenvalue Spectral SubtractionCSCJournals
In this paper, a phase space reconstruction-based method is proposed for speech enhancement. The method embeds the noisy signal into a high dimensional reconstructed phase space and uses Spectral Subtraction idea. The advantages of the proposed method are fast performance, high SNR and good MOS. In order to evaluate the proposed method, ten signals of TIMIT database mixed with the white additive Gaussian noise and then the method was implemented. The efficiency of the proposed method was evaluated by using qualitative and quantitative criteria.
Information Content of Complex NetworksHector Zenil
This short talk given in Stockholm, Sweden, explains how algorithmic complexity measures, notably Kolmogorov complexity approximated both by lossless compression algorithms and the Block Decomposition Method (BDM) are capable of characterizing graphs and networks by some of their group-theoretic and topological properties, notably graph automorphism group size and clustering coefficients of complex networks. The method distinguished between models of networks such as regular, random, small-world and scale-free.
Digital camera and mobile document image acquisition are new trends arising in the world of Optical Character Recognition and text detection. In some cases, such process integrates many distortions and produces poorly scanned text or text-photo images and natural images, leading to an unreliable OCR digitization. In this paper, we present a novel nonparametric and unsupervised method to compensate for
undesirable document image distortions aiming to optimally improve OCR accuracy. Our approach relies on a very efficient stack of document image enhancing techniques to recover deformation of the entire document image. First, we propose a local brightness and contrast adjustment method to effectively handle lighting variations and the irregular distribution of image illumination. Second, we use an optimized greyscale conversion algorithm to transform our document image to greyscale level. Third, we sharpen the
useful information in the resulting greyscale image using Un-sharp Masking method. Finally, an optimal global binarization approach is used to prepare the final document image to OCR recognition. The proposed approach can significantly improve text detection rate and optical character recognition
accuracy. To demonstrate the efficiency of our approach, an exhaustive experimentation on a standard dataset is presented.
A novel method is proposed for image segmentation based on probabilistic field theory. This model assumes that the whole pixels of an image and some unknown parameters form a field. According to this model, the pixel labels are generated by a compound function of the field. The main novelty of this model is it consider the features of the pixels and the interdependent among the pixels. The parameters are generated by a novel spatially variant mixture model and estimated by expectation-maximization (EM)-
based algorithm. Thus, we simultaneously impose the spatial smoothness on the prior knowledge. Numerical experiments are presented where the proposed method and other mixture model-based methods were tested on synthetic and real world images. These experimental results demonstrate that our algorithm achieves competitive performance compared to other methods.
ANALYSIS OF INTEREST POINTS OF CURVELET COEFFICIENTS CONTRIBUTIONS OF MICROS...sipij
This paper focuses on improved edge model based on Curvelet coefficients analysis. Curvelet transform is
a powerful tool for multiresolution representation of object with anisotropic edge. Curvelet coefficients
contributions have been analyzed using Scale Invariant Feature Transform (SIFT), commonly used to study
local structure in images. The permutation of Curvelet coefficients from original image and edges image
obtained from gradient operator is used to improve original edges. Experimental results show that this
method brings out details on edges when the decomposition scale increases.
NIRS-BASED CORTICAL ACTIVATION ANALYSIS BY TEMPORAL CROSS CORRELATIONsipij
In this study we present a method of signal processing to determine dominant channels in near infrared spectroscopy (NIRS). To compare measuring channels and identify delays between them, cross correlation is computed. Furthermore, to find out possible dominant channels, a visual inspection was performed. The
outcomes demonstrated that the visual inspection exhibited evoked-related activations in the primary somatosensory cortex (S1) after stimulation which is consistent with comparable studies and the cross correlation study discovered dominant channels on both cerebral hemispheres. The analysis also showed a relationship between dominant channels and adjacent channels. For that reason, our results present a new
method to identify dominant regions in the cerebral cortex using near-infrared spectroscopy. These findings have also implications in the decrease of channels by eliminating irrelevant channels for the experiment.
A divisive hierarchical clustering based method for indexing image informationsipij
In most practical applications of image retrieval, high-dimensional feature vectors are required, but current multi-dimensional indexing structures lose their efficiency with growth of dimensions. Our goal is to propose a divisive hierarchical clustering-based multi-dimensional indexing structure which is efficient in high-dimensional feature spaces. A projection pursuit method has been used for finding a component of the data, which data's projections onto it maximizes the approximation of negentropy for preparing essential information in order to partitioning of the data space. Various tests and experimental results on high-dimensional datasets indicate the performance of proposed method in comparison with others.
The state-of-the-art Automatic Speech Recognition (ASR) systems lack the ability to identify spoken words if they have non-standard pronunciations. In this paper, we present a new classification algorithm to identify pronunciation variants. It uses Dynamic Phone Warping (DPW) technique to compute the
pronunciation-by-pronunciation phonetic distance and a threshold critical distance criterion for the classification. The proposed method consists of two steps; a training step to estimate a critical distance
parameter using transcribed data and in the second step, use this critical distance criterion to classify the input utterances into the pronunciation variants and OOV words.
The algorithm is implemented using Java language. The classifier is trained on data sets from TIMIT
speech corpus and CMU pronunciation dictionary. The confusion matrix and precision, recall and accuracy performance metrics are used for the performance evaluation. Experimental results show significant performance improvement over the existing classifiers.
My Best friend Izaiah Kaine Breeden....recently left this world..and he got what he wanted..he is flying with the angels.....Rest In Paradise Sweet bubbles...Buttercup loves you</3
USING CRM SOLUTIONS TO INCREASE SALES AND RETAIN VALUABLE CUSTOMERSInsideUp
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If you need further information, or would like assistance in selecting a CRM provider, go to
http://www.insideup.com/compare/customer_relationship_management
Routing in All-Optical Networks Using Recursive State Space Techniquesipij
In this papr, we have minimized the effects of failures on network performace, by using suitable Routing
and Wavelenghth Assignment(RWA) method without disturbing other performance criteria such as blocking
probability(BP) and network management(NM). The computation complexity is reduced by using Kalaman
Filter(KF) techniques. The minimum reconfiguration probability routing (MRPR) algorithm must be
able to select most reliable routes and assign wavelengths to connections in a manner that utilizes the light
path(LP) established efficiently considering all possible requests.
A modified distance regularized level set model for liver segmentation from c...sipij
Segmentation of organs from medical images is an active and interesting area of research. Liver segmentation incurs more challenges and difficulties compared with segmentation of other organs. In this paper we demonstrate a liver segmentation method for computer tomography images. We revisit the distance regularization level set (DRLS) model by deploying new balloon forces. These forces control the direction of the evolution and slow down the evolution process in regions that are associated with weak or without edges. The newly added balloon forces discourage the evolving contour from exceeding the liver
boundary or leaking at a region that is associated with a weak edge, or does not have an edge. Our
experimental results confirm that the method yields a satisfactory overall segmentation outcome. Comparing with the original DRLS model, our model is proven to be more effective in handling oversegmentation problems.
In this paper, a new algorithm for a high resolution
Direction Of Arrival (DOA) estimation method for multiple
wideband signals is proposed. The proposed method proceeds
in two steps. In the first step, the received signals data is
decomposed in a Toeplitz form using the first-order statistics.
In the second step, The QR decomposition is applied on the
constructed Toeplitz matrix. Compared with existing schemes,
the proposed scheme provides several advantages. First, it
requires computing the triangular matrix R or the orthogonal
matrix Q to find the DOA; these matrices can be computed
with O(n2) operation. However, most of the existing schemes
required eignvalue decomposition (EVD) for the covariance
matrix or singular value decomposition (SVD) for the data
matrix; using EVD or SVD requires much more complex
computational O(n3) operation. Second, the proposed scheme
is more suitable for high-speed communication since it
requires first-order statistics and a single snapshot. Third,
the proposed scheme can estimate the correlated wideband
signals without using spatial smoothing techniques; whereas,
already-existing schemes do not. Accuracy of the proposed
wideband DOA estimation method is evaluated through
computer simulation in comparison with a conventional
method.
Accurate indoor positioning system based on modify nearest point techniqueIJECEIAES
Wireless fidelity (Wi-Fi) is common technology for indoor environments that use to estimate required distances, to be used for indoor localization. Due to multiple source of noise and interference with other signal, the receive signal strength (RSS) measurements unstable. The impression about targets environments should be available to estimate accurate targets location. The Wi-Fi fingerprint technique is widely implemented to build database matching with real data, but the challenges are the way of collect accurate data to be the reference and the impact of different environments on signals measurements. In this paper, optimum system proposed based on modify nearest point (MNP). To implement the proposal, 78 points measured to be the reference points recorded in each environment around the targets. Also, the case study building is separated to 7 areas, where the segmentation of environments leads to ability of dynamic parameters assignments. Moreover, database based on optimum data collected at each time using 63 samples in each point and the average will be final measurements. Then, the nearest point into specific environment has been determined by compared with at least four points. The results show that the errors of indoor localization were less than (0.102 m).
Recent advances in radio and embedded systems for completing the procedure of location estimation most
of the time sensor networks are fully dependent on the distance measurements that is present between the
sensor neighbourhood node. Techniques used for the localization can be categorized differently.
Techniques used for the measurement of the distance between the wireless sensor nodes, dependent upon
the physical means are divided into three broader categories namely Received signal strength (RSS), Angle
of Arrival (AOA) and propagation base on time measurements. This paper discusses the most of the
approached of WSN and IoT based positioning system.
Investigations on real time RSSI based outdoor target tracking using kalman f...IJECEIAES
Target tracking is essential for localization and many other applications in Wireless Sensor Networks (WSNs). Kalman filter is used to reduce measurement noise in target tracking. In this research TelosB motes are used to measure Received Signal Strength Indication (RSSI). RSSI measurement doesn‟t require any external hardware compare to other distance estimation methods such as Time of Arrival (TOA), Time Difference of Arrival (TDoA) and Angle of Arrival (AoA). Distances between beacon and non-anchor nodes are estimated using the measured RSSI values. Position of the nonanchor node is estimated after finding the distance between beacon and nonanchor nodes. A new algorithm is proposed with Kalman filter for location estimation and target tracking in order to improve localization accuracy called as MoteTrack InOut system. This system is implemented in real time for indoor and outdoor tracking. Localization error reduction obtained in an outdoor environment is 75%.
CHANNEL ESTIMATION AND MULTIUSER DETECTION IN ASYNCHRONOUS SATELLITE COMMUNIC...ijwmn
In this paper, we propose a new method of channel estimation for asynchronous additive white Gaussian noise channels in satellite communications. This method is based on signals correlation and multiuser interference cancellation which adopts a successive structure. Propagation delays and signals amplitudes are jointly estimated in order to be used for data detection at the receiver. As, a multiuser detector, a single stage successive interference cancellation (SIC) architecture is analyzed and integrated to the channel estimation technique and the whole system is evaluated. The satellite access method adopted is the direct sequence code division multiple access (DS CDMA) one. To evaluate the channel estimation and the detection technique, we have simulated a satellite uplink with an asynchronous multiuser access.
Using Subspace Pursuit Algorithm to Improve Performance of the Distributed Co...Polytechnique Montreal
This paper applies a compressed algorithm to improve the spectrum sensing performance of cognitive radio technology.
At the fusion center, the recovery error in the analog to information converter (AIC) when reconstructing the
transmit signal from the received time-discrete signal causes degradation of the detection performance. Therefore, we
propose a subspace pursuit (SP) algorithm to reduce the recovery error and thereby enhance the detection performance.
In this study, we employ a wide-band, low SNR, distributed compressed sensing regime to analyze and evaluate the
proposed approach. Simulations are provided to demonstrate the performance of the proposed algorithm.
Further results on the joint time delay and frequency estimation without eige...IJCNCJournal
Joint Time Delay and Frequency Estimation (JTDFE) problem of complex sinusoidal signals received at
two separated sensors is an attractive problem that has been considered for several engineering
applications. In this paper, a high resolution null (noise) subspace method without eigenvalue
decomposition is proposed. The direct data Matrix is replaced by an upper triangular matrix obtained from
Rank-Revealing LU (RRLU) factorization. The RRLU provides accurate information about the rank and the
numerical null space which make it a valuable tool in numerical linear algebra.The proposed novel method
decreases the computational complexity of JTDFE approximately to the half compared with RRQR
methods. The proposed method generates estimates of the unknown parameters which are based on the
observation and/or covariance matrices. This leads to a significant improvement in the computational load.
Computer simulations are included in this paper to demonstrate the proposed method.
Enhanced Mobile Node Tracking With Received Signal Strength in Wireless Senso...IOSR Journals
Abstract : Node localization is important parameter in WSN. Node localization is required to report origin of
events which makes it one of the important challenges in WSN. Received signal strength (RSS) is used to
calculate distance between mobile node and reference node. The position of the mobile node is calculated using
multilateration algorithm (MA). Extended Kalman filter (EKF) is utilized to estimate the actual position. In this
paper, the implementation and enhancement of a tracking system based on RSS indicator with the aid of an
Extended Kalman Filter (EKF) is described and an adaptive filter is derived.
Keywords - Extended Kalman filter (EKF), mobile node tracking, multilateration algorithm (MA), received
signal strength (RSS), Wireless sensor networks (WSN)
Enhanced Mobile Node Tracking With Received Signal Strength in Wireless Senso...IOSR Journals
Node localization is important parameter in WSN. Node localization is required to report origin of
events which makes it one of the important challenges in WSN. Received signal strength (RSS) is used to
calculate distance between mobile node and reference node. The position of the mobile node is calculated using
multilateration algorithm (MA). Extended Kalman filter (EKF) is utilized to estimate the actual position. In this
paper, the implementation and enhancement of a tracking system based on RSS indicator with the aid of an
Extended Kalman Filter (EKF) is described and an adaptive filter is derived.
NONLINEAR MODELING AND ANALYSIS OF WSN NODE LOCALIZATION METHODijwmn
In this paper, node localization algorithms in wireless sensor networks are researched, the traditional algorithms are studied, and some meaningful results are obtained. For the localization algorithm and route planning of WSN exists a big localization error in wireless communication. WSN communication system is researched. According to the anchor nodes and unknown nodes, a new localization algorithm and route planning method of WSN are proposed in this paper. At the same time, a new genetic algorithm of route planning of WSN is proposed. The performance of the node density and localization error is simulated and analyzed. The simulation results show that the performance of proposed WSN localization algorithm and route planning method are better than the traditional algorithms.
NONLINEAR MODELING AND ANALYSIS OF WSN NODE LOCALIZATION METHODijwmn
In this paper, node localization algorithms in wireless sensor networks are researched, the traditional
algorithms are studied, and some meaningful results are obtained. For the localization algorithm and route
planning of WSN exists a big localization error in wireless communication. WSN communication system is
researched. According to the anchor nodes and unknown nodes, a new localization algorithm and route
planning method of WSN are proposed in this paper. At the same time, a new genetic algorithm of route
planning of WSN is proposed. The performance of the node density and localization error is simulated and
analyzed. The simulation results show that the performance of proposed WSN localization algorithm and
route planning method are better than the traditional algorithms.
Mimo radar detection in compound gaussian clutter using orthogonal discrete f...ijma
This paper proposes orthogonal Discrete Frequency Coding Space Time Waveforms (DFCSTW) for
Multiple Input and Multiple Output (MIMO) radar detection in compound Gaussian clutter. The proposed
orthogonal waveforms are designed considering the position and angle of the transmitting antenna when
viewed from origin. These orthogonally optimized show good resolution in spikier clutter with Generalized
Likelihood Ratio Test (GLRT) detector. The simulation results show that this waveform provides better
detection performance in spikier Clutter.
Fixed Point Realization of Iterative LR-Aided Soft MIMO Decoding AlgorithmCSCJournals
Multiple-input multiple-output (MIMO) systems have been widely acclaimed in order to provide high data rates. Recently Lattice Reduction (LR) aided detectors have been proposed to achieve near Maximum Likelihood (ML) performance with low complexity. In this paper, we develop the fixed point design of an iterative soft decision based LR-aided K-best decoder, which reduces the complexity of existing sphere decoder. A simulation based word-length optimization is presented for physical implementation of the K-best decoder. Simulations show that the fixed point result of 16 bit precision can keep bit error rate (BER) degradation within 0.3 dB for 8×8 MIMO systems with different modulation schemes.
Ill-posedness formulation of the emission source localization in the radio- d...Ahmed Ammar Rebai PhD
To contact the authors : tarek.salhi@gmail.com and ahmed.rebai2@gmail.com
In the field of radio detection in astroparticle physics, many studies have shown the strong dependence of the solution of the radio-transient sources localization problem (the radio-shower time of arrival on antennas) such solutions are purely numerical artifacts. Based on a detailed analysis of some already published results of radio-detection experiments like : CODALEMA 3 in France, AERA in Argentina and TREND in China, we demonstrate the ill-posed character of this problem in the sens of Hadamard. Two approaches have been used as the existence of solutions degeneration and the bad conditioning of the mathematical formulation problem. A comparison between experimental results and simulations have been made, to highlight the mathematical studies. Many properties of the non-linear least square function are discussed such as the configuration of the set of solutions and the bias.
Similar to Time of arrival based localization in wireless sensor networks a non linear approach (20)
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UiPath Test Automation using UiPath Test Suite series, part 3
Time of arrival based localization in wireless sensor networks a non linear approach
1. Signal & Image Processing : An International Journal (SIPIJ) Vol.6, No.1, February 2015
DOI : 10.5121/sipij.2015.6104 45
TIME OF ARRIVAL BASED LOCALIZATION IN
WIRELESS SENSOR NETWORKS: A NON-LINEAR
APPROACH
Ravindra. S1
and Jagadeesha S N2
1,2
Department of Computer Science and Engineering,
Jawaharlal Nehru National College of Engineering, Shimoga -577204.
Visvesvaraya Technological University,
Belgaum, Karnataka,
India.
ABSTRACT
In this paper, we aim to obtain the location information of a sensor node deployed in a Wireless Sensor
Network (WSN). Here, Time of Arrival based localization technique is considered. We calculate the
position information of an unknown sensor node using the non- linear techniques. The performances of the
techniques are compared with the Cramer Rao Lower bound (CRLB). Non-linear Least Squares and the
Maximum Likelihood are the non-linear techniques that have been used to estimate the position of the
unknown sensor node. Each of these non-linear techniques are iterative approaches, namely, Newton
Raphson estimate, Gauss Newton Estimate and the Steepest Descent estimate for comparison. Based on the
results of the simulation, the approaches have been compared. From the simulation study, Localization
based on Maximum Likelihood approach is having higher localization accuracy.
KEYWORDS
Node Localization, Time of Arrival, Maximum Likelihood approach, Non-linear Least Squares approach
and CRLB.
1. INTRODUCTION
Location awareness has gained great interest in many wireless communication systems such as
mobile cellular networks, wireless local area networks and wireless sensor networks because of
its wide range of applications and add-ons [1]. Location information based services such as
position based social networking, location based advertisement and E-911 emergency services
have become more important in order to enhance the future life style [2]. Wireless sensor
networks are a group of large number of small, low cost, low energy and multi-task sensors
capable of many functions such as sensing, computing and communication between these
wireless devices deployed in a very large geographic area [3][4][5][6].
Because of the developments in wireless systems, communication in wireless sensor networks has
become a potential area of research over the last decade [7][8][9]. Many applications in sensor
networks need the location information of these tiny sensors, data collected from these sensors are
of limited use if the information is without the location information of the sensor. Despite the
huge research, still a reliable or well accepted technique to obtain the location information is yet
to be realized [10][11][12].
2. Signal & Image Processing : An International Journal (SIPIJ) Vol.6, No.1, February 2015
46
Since the sensor nodes are cost effective and also when deployed, they are in a large number, it is
not practical to have Global Positioning Systems (GPS) receivers equipped sensors to obtain the
position information [13]. Moreover GPS receivers are too costly [14]. Many localization
techniques have been proposed in literature, but there is not a single approach which is simple and
distinct which gives a better solution for sensor networks.
The basic approaches for measuring the location information in wireless sensor networks are
Time of Arrival (TOA), Angle of Arrival (AOA), Received Signal Strength (RSS) and Time
Difference of Arrival (TDOA)[15][16][17][18]. Of these approaches, the TOA, TDOA and RSS
gives the distance measurements while the AOA gives both distance and angle measurements.
The approaches seem to be simple but calculating the angle and distance information is not
simple because of the Non-linear relationships with the source node.
The main goal of this paper is calculating the location information based on the Time of Arrival
technique. Further the performance of the various non-linear techniques such as Non linear least
squares and Maximum likelihood techniques are compared with the Cramer Rao Lower bound.
Both the nonlinear techniques employ iterative Newton Raphson, Gauss Newton and Steepest
Descent methods. We assume a two dimensional rectangular area, where the sensors are deployed
in Line of Sight (LOS) scenario [19].
The rest of the paper is organized as follows. In section 2, we present the mathematical
measurement model of time of arrival based localization technique and its positioning principles.
The Non-Linear approaches are discussed in section 3 and Simulation results are presented in
Section 4. Finally Conclusions are drawn in section 5.
2. MATHEMATICAL MEASUREMENT MODEL.
The measurement model for Time of arrival based node localization approach is given by:
( )= +r f x n (1)
Where ‘r’ is the measurement vector, ‘x’ is the position of the source to be estimated, ‘n’ is the
additive zero mean Gaussian vector and f(x) is a nonlinear function.
2.1. Time of Arrival
Localization is the mechanism of obtaining the exact location of an unknown sensor node from
known nodes (anchors or beacons) by means of the intersection of three or more measurements
from the known nodes. For the TOA based technique, the distance information is extracted from
the propagation delay between the transmitter and the receiver. This technique can be further
classified into two approaches namely, One- way ranging TOA and Two way ranging TOA[20].
The former approach requires perfect synchronization between the transmitter and the receiver,
while the latter does not require synchronization between the transmitter and the receiver. We
consider synchronous networks and the broadcast mode for anchor transmission in this paper.
The measured TOA represents a circle with its centre at the receiver and the source must lie on
the circumference in a 2 dimensional space. A minimum of three or more circles are needed for
two dimensional position estimate [21]. If the number of sensors is less than three, there is a
possibility that there may not be any intersecting points and hence not a feasible solution. With
the knowledge of the sensor array geometry the source position can be estimated based on the
optimization criterion and these can be represented as a set of circular equations.
3. Signal & Image Processing : An International Journal (SIPIJ) Vol.6, No.1, February 2015
47
Let [ ]
T
x y=x be the unknown sensor source position and [ ]
T
k k kx y=x be the known
coordinates of the kth
anchor, where k=1, 2,….., K. the number of receivers K must be greater
than or equal to three. The distance between the source and the kth
senor, denoted by kd , is
simply:
( ) ( )
2 2
2
, 1,2,.....,k k k kd x x y y k K= − = − + − =x x (2)
The source transmits a signal at time 0 and the kth
sensor receives it at time tk., i.e., there is a
simple relationship between the measured TOAs and the distance, and is represented as
, 1,2,...k
k
d
t k K
c
= = (3)
Where ‘c’ is the velocity of light. The measured TOAs have small errors; hence the range based
measurement model is modelled as:
( ) ( )
, ,
2 2
, , 1,2,.....,
TOA k k TOA k
k k TOA k
r d n
x x y y n k K
= +
= − + − + =
(4)
Where ,TOA kn is the range error of the measured TOAs. Complete derivation of the TOA function
is presented in our earlier communication [22].
The source position measurement problem based on the obtained TOA values is to calculate x
given ,TOA kr . It is assumed that the range errors { },TOA kn are uncorrelated Gaussian processes with
variances{ }2
,TOA kσ and zero mean. It is known that zero mean property indicates Line of sight
transmission. The probability density function for each of the random variable ,TOA kr denoted by
( ),TOA kP r has the form
( ) ( )
2
, ,22
,,
1 1
exp
22
TOA k TOA k k
TOA kTOA k
P r r d
π
= − − σσ
(5)
In other words, we can write, ( )2
, ,,TOA k k TOA kr d≈ σ¥ which are the mean and variances. And the
PDF is given by
( )
( )
( ) ( )1
1/2/2
1 1
exp
22
T
TOA TOA TOA TOAK
TOA
P r d d
π
−
= − − −
r C r
C
(6)
Where ( )2 2 2
,1 ,2 ,, ,......,TOA TOA TOA TOA Kdiag= σ σ σC .
4. Signal & Image Processing : An International Journal (SIPIJ) Vol.6, No.1, February 2015
48
2.2. Cramer Rao Lower Bound
The CRLB, in general can be defined as the theoretical lower bound on the variance of any
unbiased estimator of an unknown parameter [23][24]. The factors affecting the TOA based
position estimation are position of the anchors, unknown source node and the measurement noise
variances [25]. The CRLB is calculated using the Fisher Information Matrix (FIM) whose
elements are defined as
( )
( )2
ln
T
p
E
x x
∂
=
∂ ∂
r
I x (7)
Using the PDF in equation (6), the FIM can be calculated as
( )
( ) ( )( )
( )( ) ( )
2 2 2 2
1 1, ,
2 2 2 2
1 1, ,
K K
k k k
k kTOA k k TOA k k
TOA K K
k k k
k kTOA k k TOA k k
x x x x y y
d d
x x y y y y
d d
= =
= =
− − −
σ σ =
− − −
σ σ
∑ ∑
∑ ∑
I X (8)
Where the lower bound for x and y are denoted by 1
1,1
( )−
I x and 1
2,2
( )−
I x respectively,
and the CRLBTOA(x), is
1 1
1,1 2,2
( ) ( ) ( )TOACRLB − −
= + x I x I x (9)
3. NON LINEAR APPROACHES FOR SOURCE LOCALIZATION
The Non-Linear approach directly uses equation (1) to solve for x by minimising the least squares
cost function obtained from the error function:
(10)
Where is the optimization variable for x, which represents the nonlinear least
squares or the Maximum Likelihood estimators, respectively and the global convergence of these
schemes is not assured since their optimization cost functions are multi-modal [26].
In this paper two Non-Linear positioning approaches, namely, Nonlinear Least Squares (NLS)
and Maximum Likelihood are presented. Further these two Non-Linear approaches use three
different iterative local search algorithms, namely, Newton Raphson algorithm, Gauss Newton
algorithm and Steepest Descent Algorithm.
Figure 1: Various Iterative approaches in NLS and ML algorithms.
NLS or ML
Steepest Descent
method
Newton Raphson
method
Gauss Newton
method
5. Signal & Image Processing : An International Journal (SIPIJ) Vol.6, No.1, February 2015
49
3.1. Nonlinear Least Squares (NLS) approach for TOA based positioning
The nonlinear least squares method is simple and is an obvious choice when the noise information
is not available. The NLS approach directly tries to minimize the least square cost function
obtained from equation (4) and is as follows:
Based on equation (4), the NLS cost function is represented as:
(11)
The smallest value in the NLS cost function is equal to the NLS position estimate
and is given by:
(12)
And obtaining ˆx is not easy, since both the local and global minimum values are contained in 2
dimensional surface of .
In order to obtain the value of ˆx there are two ways, the first method is to use iterative local
search algorithms based on an initial position estimate 0
ˆx , where 0 refers to the iteration number.
If the value obtained after 0th
iteration is approximately close to x, then ˆx can be iterated to the
closest value of x. and second method is using the random search or the grid search techniques
namely, the Genetic Algorithm (GA)[27] and Particle Swarm Optimization (PSO)[28][29]. In this
paper, we limit our studies to the iterative search techniques.
The three iterative approaches, namely, Newton Raphson iterative procedure, Gauss Newton
iterative procedure, and Steepest Descent iterative procedure, are presented in the following
subsections.
3.1.1. Newton Raphson Iterative procedure
The iterative Newton – Raphson procedure for ˆx is given by:
( )( ) ( )( )1 1
, ,
ˆ ˆ ˆ ˆm m m m
NLS TOA NLS TOAJ J+ −
= − ∇x x H x x (13)
Where ( )( ),
ˆm
NLS TOAJH x is the Hessian Matrix and ( )( ),
ˆm
NLS TOAJ∇ x is the gradient vector
measured at the m th
iteration estimate.
The algorithm for the Newton raphson procedure is summarized in Table 1.
3.1.2. Gauss Newton Iterative procedure
The iterative Gauss - Newton procedure for ˆx is given by:
( )( )( ) ( )( ) ( )( )
1
1
ˆ ˆ ˆ ˆ ˆm m T m T m m
TOA TOA TOA TOA
−
+
= + −x x G f x G f x r f x (14)
Where ( )( )ˆm
TOAG f x is the jacobian matrix of ( )ˆm
TOAf x calculated at ˆm
x .
The algorithm for the Gauss- Newton iterative procedure is summarized in Table 2:
6. Signal & Image Processing : An International Journal (SIPIJ) Vol.6, No.1, February 2015
50
Table 1: Newton Raphson Iterative Procedure algorithm
Algorithm 1: Newton Raphson Method
Input: K = number of anchor nodes
M = number of iterations
X = {set containing all anchor nodes};
Initialize µ
Output: estimated coordinates
X (M) = [x1, y1] T
Initialization: a random point x (0)
For i=1: M do
For j=1: K do
H =
డమಿಽೄ,ೀಲሺ௫ሻ
డ௫మ
డమಿಽೄ,ೀಲሺ௫ሻ
డ௫డ௬
డమಿಽೄ,ೀಲሺ௫ሻ
డ௫డ௬
డమಿಽೄ,ೀಲሺ௫ሻ
డ௬మ
End
G = ൦
డቀಿಽೄ,ೀಲሺ௫ሻቁ
డ௫
డቀಿಽೄ,ೀಲሺ௫ሻቁ
డ௬
൪
1
G−
= −X x H
End
Table 2: Gauss Newton Iterative Procedure algorithm
Algorithm 2: Gauss Newton method
Input: K = number of anchor nodes
M = number of iterations
X = {set containing all anchor nodes};
Output: estimated coordinates
X (M) = [x1, y1] T
Initialization: a random point x (0)
For i=1: M do
For j=1: K do
G =
ۏ
ێ
ێ
ێ
ێ
ێ
ێ
ۍ
డඥሺ௫ି௫భሻమାሺ௬ି௬భሻమ
డ௫
డඥሺ௫ି௫భሻమାሺ௬ି௬భሻమ
డ௬
డඥሺ௫ି௫మሻమାሺ௬ି௬మሻమ
డ௫
డඥሺ௫ି௫మሻమାሺ௬ି௬మሻమ
డ௬
.
.
డඥሺ௫ି௫ಽሻమାሺ௬ି௬ಽሻమ
డ௫
డඥሺ௫ି௫ಽሻమାሺ௬ି௬ಽሻమ
డ௬ ے
ۑ
ۑ
ۑ
ۑ
ۑ
ۑ
ې
End
( ) ( )
1
G G G ( ) ;T T
r f x
−
= + −X x
End
3.1.3. Steepest Descent Iterative procedure
The iterative procedure for the Steepest Descent iterative procedure is given by:
( )( )1
,
ˆ ˆ ˆm m m
NLS TOAJµ+
= − ∇x x x (15)
Where µ is a positive constant which commands the convergence rate and stability.
7. Signal & Image Processing : An International Journal (SIPIJ) Vol.6, No.1, February 2015
51
Table 3, summarizes the algorithm for Steepest Descent iterative procedure.
Table 3: Steepest Descent Iterative Procedure algorithm
Algorithm 3: Steepest Descent method
Input: K = number of anchor nodes
M = number of iterations
X = {set containing all anchor nodes};
Initialize µ
Output: estimated coordinates
X (m) = [x1, y1] T
Initialization: a random point x (0)
For i=1: M do
For j=1: K do
G = ൦
డቀಿಽೄ,ೀಲሺ௫ሻቁ
డ௫
డቀಿಽೄ,ೀಲሺ௫ሻቁ
డ௬
൪
End
G= − µX x
End
3.2. Maximum Likelihood approach for TOA based positioning
The ML method maximises the probability density functions of the measured TOAs under the
assumption that error distribution is known [30][31][32][33][34]. The maximization of the
measured TOAs using ML method corresponds to the weighted version of the Non-linear least
squares approach [35].
We consider the logarithmic version of the equation (6):
( )( )
( )
( ) ( )1
1/2/2
1 1
ln ln ln
22
T
TOA TOA TOA TOAL
TOA
P d d
π
−
= − − −
r r C r
C
(16)
The first term in the RHS is independent of x, and maximising equation (16) is equal to
minimising the second term, and hence the ML estimate can be obtained as:
(17)
Or we can write
(18)
Where ( ). xML TOAJ % is the ML cost function, which is of the form:
8. Signal & Image Processing : An International Journal (SIPIJ) Vol.6, No.1, February 2015
52
(19)
The ML estimator is generalized to NLS method because under the assumption of zero mean
Gaussian noise, 2
,TOA kσ is large which corresponds to a large noise in ,TOA kr , a small weight of
2
,1/ TOA kσ is employed in the squared term , and vice versa. And also
when
1
TOA
−
C is very near to the identity matrix or 2
, 1,2,.....,TOA k k Kσ = are identical [35].
3.2.1. Newton Raphson Iterative procedure
The iterative Newton – Raphson procedure for ˆx is given by:
( )( ) ( )( )1 1
, ,
ˆ ˆ ˆ ˆx x x xk k k k
ML TOA ML TOAJ J+ −
= − ∇H (20)
Where ( )( ),
ˆm
NLS TOAJH x is the Hessian Matrix and ( )( ),
ˆm
NLS TOAJ∇ x is the gradient vector
measured at the m th
iteration estimate.
3.2.2. Gauss Newton Iterative procedure
The iterative Gauss - Newton procedure for ˆx is given by:
( )( ) ( )( )( ) ( )( ) ( )( )
1
1 1 1
ˆ ˆ ˆ ˆ ˆ ˆx x x x x xk k T k k T k k
TOA TOA TOA TOA TOA TOA TOA
−
+ − −
= + −G f C G f G f C r f (21)
Where ( )( )ˆm
TOAG f x is the jacobian matrix of ( )ˆm
TOAf x calculated at ˆm
x .
3.2.3. Steepest Descent Iterative procedure
The iterative procedure for the Steepest Descent iterative procedure is given by:
( )( )1
,
ˆ ˆ ˆx x xk k k
ML TOAJ+
= −µ∇ (22)
Where µ is a positive constant which commands the convergence rate and stability.
4. SIMULATION AND RESULTS
Computer simulations have been carried out to evaluate the performance of the localization
algorithms. In this paper, we consider a 2D geometry of K=4 receivers with known coordinates at
(0, 0), (0, 10), (10, 10) and (10, 0), while the unknown source position is assumed to be (x, y) =
(2, 3), such that the source is located inside the square bounded by four receivers. The Signal- to -
Noise - Ratio (SNR) = 30dB has been assumed. The results of the simulations are averaged over
50 iterations. And the step size parameter µ = 0.01 is assumed for the Steepest Descent method.
All the simulations have been conducted using MATLAB [TM] Version 7.10.0.499 (R2010A)on
Microsoft Windows XP, Professional Version 2002, Service pack 3, 32 bit operating system
installed on Intel[R], Core [TM] 2 Duo CPU, E4500 @ 2.20GHz, 2.19GHz, 2.0GB of RAM.
We have estimated the X and Y position using Nonlinear Least Squares (NLS) approach. There
are three main parts in the simulation, i.e., generating the range measurements, position
9. Signal & Image Processing : An International Journal (SIPIJ) Vol.6, No.1, February 2015
53
estimation using NLS estimator which is realized by the Newton Raphson, Gauss Newton and
Steepest Descent methods and displaying the results. Figure 2(a), 2(c), and 2(e) show the X-
estimate using Newton Raphson, Gauss Newton and Steepest Descent methods whereas Figure.
2(b), 2(d), and 2(f) show the Y- estimate using Newton Raphson, Gauss Newton and Steepest
Descent methods respectively. In the 2D geometry, the x-axis represents the number of iterations,
i.e., 50 iterations at 30 dB, whereas the y-axis represents the position of the both X and Y
estimate. The initial guess for estimating the position is assumed at (3, 2). All the schemes
provide the same position estimate upon convergence. It can be seen that the Newton Raphson
and Gauss Newton methods converge in about 3 iterations while the steepest descent algorithm
needs approximately 15 iterations to converge.
We have estimated the X and Y position using Maximum Likelihood (ML) approach. And the
structure of the simulation is same as in the first example where there are three main parts in the
simulation i.e., generating the range measurements, position estimation using ML estimator which
is realized by the Newton Raphson, Gauss Newton and Steepest Descent methods and displaying
the results. Figure 3(a), 3(c), and 3(e) shows the X- estimate using Newton Raphson, Gauss
Newton and Steepest Descent methods whereas Figure 3(b), 3(d), and 3(f) shows the Y- estimate
in Newton Raphson, Gauss Newton and Steepest Descent methods respectively. In the 2D
geometry the x-axis represents the number of iterations i.e., 50 iterations at 30 dB, whereas the y-
axis represents the position of the both X and Y estimate. The initial guess for estimating the
position is assumed at (3, 2). similar to the NLS approach, all the schemes provide the same
position estimate upon convergence, it can be seen the Newton Raphson and Gauss Newton
methods converge faster than the steepest Descent algorithm, Nevertheless, it is difficult to see
that the Maximum Likelihood (ML) estimator is superior to the NLS approach in terms of
positioning accuracy based on a single run.
Table 4: Estimated TOA Position measurement for NLS method when the Number of Anchor Nodes, n=4
Method : NLS Time of Arrival Estimate
X- Position Estimate in
Meters
Y- Position Estimate in
Meters
Newton Raphson Approach 1.97 2.96
Gauss Newton Approach 2.09 3.04
Steepest Descent Approach 2.07 2.99
Table 5: Estimated TOA Position measurement for ML method when the Number of Anchor Nodes, n=4
Method : ML Time of Arrival Estimate
X- Position Estimate in
Meters
Y- Position Estimate in
Meters
Newton Raphson Approach 1.98 2.97
Gauss Newton Approach 2.01 3.01
Steepest Descent Approach 2.05 3.07
The result of the position measurement estimate of the both NLS and ML method is given in
Table 4 and Table 5 respectively. From Table 4, it can be seen that all the approaches estimate
the X and Y positions, and the results of the Gauss Newton approach is better when compared
with the other approaches. From Table 5, it is clear that the Gauss Newton method is near to the
true positions and is having higher localization accuracy and the other approaches have lower
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54
localization accuracy. Comparing Table 4 and 5, the iterative Gauss Newton method of the
Maximum Likelihood approach is the better approach having higher localization accuracy.
(a). Newton Raphson X estimate (b). Newton Raphson Y estimate
(c). Gauss Newton X estimate (d). Gauss Newton Y estimate
(e). Steepest Descent X estimate (f). Steepest Descent Y estimate
Figure 2: Estimate of X and Y position Versus Number of Iterations in NLS Approach
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(a). Newton Raphson X estimate (b). Newton Raphson Y estimate
(c). Gauss Newton X estimate (d). Gauss Newton Y estimate
(e). Steepest Descent X estimate (f). Steepest Descent Y estimate
Figure 3: Estimate of X and Y position Versus Number of Iterations in ML Approach
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(a). MSPE Newton Raphson approach
(b). MSPE Gauss Newton approach
(c). MSPE Steepest Descent approach
Figure 4: Mean Square Position Error Comparison of Non-Linear Approaches
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We estimate the Mean Square Position Error (MSPE) comparison of NLS and ML approaches
with Cramer Rao Lower Bound for Time of Arrival based positioning, the range error variance
2
,TOA kσ is proportional to
2
kd with Signal to Noise Ratio (SNR) = 2 2
,/k TOA kd σ . The MSPE is
defined as ( ) ( ){ }2 2
ˆ ˆE x x y y− + − . Here we compute the empirical MSPE based on 1000
independent runs, which is given as ( ) ( )
1000 2 2
1
ˆ ˆ /1000i
x x y y=
− + −
∑ where( )ˆ ˆ,i ix y , denotes
the position estimate of the i th
run. The simulation is run for both the NLS and ML approaches
for the iterative Newton Raphson, Gauss Newton and the Steepest Descent Techniques and the
function for the CRLB is also included, and the results are depicted in Figure 4(a), 4(b) and 4(c)
for the various approaches in both NLS and ML method. Both the axes employ dB scale in the
range [ ]10,40SNR∈ − dB. From the figures we say that the performance of the Gauss Newton
based ML estimator is superior and achieves optimal estimation performance, while the other
approaches are suboptimal. Hence the ML estimator is superior to the NLS approach and its
MSPE can attain CRLB.
5. CONCLUSIONS
The work presented addresses the problem of position estimation of a sensor node, using time of
arrival measurements. The Cramer Rao Lower Bound for the position estimation problem has
been presented and the two nonlinear approaches such as Nonlinear least squares and Maximum
likelihood approaches have been presented. Further both the Nonlinear Least Squares and
Maximum likelihood approaches use three iterative local search algorithms, namely, Newton
Raphson, Gauss Newton and the steepest descent methods and the results of these techniques are
compared. Extensive simulation reveals that the Maximum Likelihood approach is superior to
Nonlinear least squares approach under line of sight measurements. And the Maximum likelihood
approach based Gauss Newton method is having higher localization accuracy and its MSPE can
attain CRLB at SNR = 20dB when compared to the other approaches.
REFERENCES
[1] Ravindra. S and Jagadeesha S N, (2013), “A Nonlinear Approach for Time of Arrival Localization in
Wireless Sensor Networks”, 6th IETE National Conference on RF and Wireless IConRFW-13,
Jawaharlal Nehru National College of Engineering, Shimoga, Karnataka, India 09-11-May, pp 35-40.
[2] Yerriswamy. T and Jagadeesha S. N, (2011), “Fault Tolerant Matrix Pencil method for Direction of
Arrival Estimation”, Signal & Image Processing: An International Journal, Vol. 2, No. 3, pp. 42-46,
2011.
[3] The Federal Communications commission about “Emergency 911 wireless services”,
http://www.fcc.gov/pshs/services/911-services/enhanced911/Welcome.html
[4] Stojmenovic I, (2005), Handbook of sensor networks algorithms and architectures. John Wiley and
Sons.
[5] Karl H, Willing A, (2005), Protocols and architectures for wireless sensor networks. John Wiley and
Sons.
[6] Patwari N, Ash J N, Kyperountas S, Hero III AO, Moses RL, Correal NS, (2005), “Locating the
nodes: cooperative localization in wireless sensor networks”. IEEE Signal Process Magazine, 22:54–
69.
[7] Wang C, Xiao L. (2007), Sensor localization under limited measurement capabilities. IEEE Network,
21:16–23.
[8] Pawel Kulakowski, Javier Vales-Alonso, Esteban Egea-Lopez, Wieslaw Ludwin and Joan Garcia-
Haro, (2010), “ Angle of Arrival localization based on antenna arrays for wireless sensor networks”,
article in press, Computers and Electrical Engineering Journal Elsevier,
doi:10.1016/j.compeleceng.2010.03.007
[9] H. Wymeersch, J. Lien and M. Z. Win, (2009), “Cooperative localization in wireless networks,”
IEEE Signal Processing Mag., vol.97, no.2, pp.427-450.
14. Signal & Image Processing : An International Journal (SIPIJ) Vol.6, No.1, February 2015
58
[10] R Zekavat and R. M Buehrer (2011), “Handbook on Wireless Position estimation,” Hoboken, NJ:
John Wiley & Sons, INC.
[11] A. H. Sayed, A. Tarighat, and N. Khajehnouri, (2005), “ Network based wireless location: Challenges
faced in developing techniques for accurate wireless location information,” IEEE Signal Processing.
Mag., Vol. 22, no. 4, pp, 24-40.
[12] Neal Patwari, Alfred O Hero III, Matt Perkins, Neiyer S Correal and Robert J O’dea. (2003),
“Relative location estimation in Wireless sensor networks”. IEEE Transactions on Signal Processing,
51(8):2137-2148.
[13] Yerriswamy. T and Jagadeesha S. N, (2012), “IFKSA- ESPRIT – Estimating the Direction of Arrival
under the Element Failures in a Uniform Linear Antenna Array”, ACEEE International Journal on
Signal & Image Processing, Vol. 3, No. 1, pp. 42 – 46, 2012.
[14] Gezici S, Tian Z, Giannakis GB, Kobayashi H, Molisch AF, Poor HV, et al. (2005), “Localization via
ultra-wideband radios,” IEEE Signal Process Mag.; 22:70–84.
[15] A. Jagoe (2003), Mobile Location Service: The Definitive Guide, Upper Saddle River: Prentice- Hall.
[16] M. Ilyas and I. Mahgoub, (2005), Handbook of Sensor Networks: Algorithms and Architectures, New
York: Wiley.
[17] J. C. Liberti and T.S. Rappaport, (1990), Smart Antennas for Wireless Communications: IS-95 and
Third Generation CDMA Applications, Upper Saddle River: Prentice- Hall.
[18] Yerriswamy. T and S. N. Jagadeesha, (2012) “Joint Azimuth and Elevation angle estimation using
Incomplete data generated by Faulty Antenna Array”, Signal & Image Processing: An International
Journal, Vol. 3, No. 6, pp. 99-114, December 2012.
[19] Theodore S Rappaport et al (1996), Wireless Communication: principles and practice, Vol. 2, Prentice
Hall PTR New Jersy.
[20] S. M. Kay, (1988), Modern Spectral estimation: Theory and Application. Englewood Cliffs, NJ:
Prentice Hall.
[21] S. M. Kay, (1993), Fundamentals of Statistical Signal Processing: Estimation Theory, Prentice- Hall.
[22] Ravindra. S and Jagadeesha S N, “Time of Arrival Based Localization in Wireless Sensor Networks:
A Linear Approach”, Signal and Image Processing: An International Journal (SIPIJ), Volume 4,
Number 4, pp 13-30, August 2013. DOI: 10.5121/sipij.2013.4402
[23] Y. Huang and J. Benesty, Eds., (2004), Audio Signal Processing for Next Generation Multimedia
Communication systems, Kluwer Academic Publishers.
[24] E. G. Larsson, ( 2004), “Cramer-Roa Bound analysis of distributed positioning in sensor networks,”
IEEE Signal processing Lett., Vol. 11, no. 3, pp. 334-337, mar, 2004.
[25] Feng Yin, Carsten Fritsche, Fredrik Gustafsson and Abdelhak M Zoubir, (2013) “ TOA- Based
Robust Wireless Geolocation and Cramer Rao Lower Bound Analysis in Harsh LOS/NLOS
Environments, IEEE Transactions on Signal Processing, (61),9,2243-2255.
http://dx.doi.org/10.1109/TSP.2013.2251341
[26] D. J. Torrieri, (1984) “Statistical theory of passive location systems,” IEEE Transactions on
Aerospace and Electronic Systems, vol 20, no.2, pp. 183-198.
[27] K. W. Cheung, H.C. So, W.-K. Ma and Y.T.Chan, (2004) “Least squares algorithms for time-of-
arrival based mobile location,” IEEE Transactions on Signal Processing, vol.52, no.4, pp.1121-1128.
[28] C. Mensing and S. Plass (2006), “Positioning algorithms for cellular networks using TDOA,” Proc.
IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 4, pp. 513-516,
Toulouse, France.
[29] H. C. So and K W Chan, (2005) “A generalized subspace approach for mobile positioning with time-
of-arrival measurements,” IEEE transactions on Signal Processing, Vol.53, no.2,pp 460-470.
[30] H.-W. Wei, Q. Wan, Z.-X. Chen and S. -F. Ye, (2008) “Multidimensional Scaling – based passive
emitter localization from range-difference measurements,” IET Signal Processing, vol.2, no.4,
pp.415-423.
[31] F.K.W. Chan, H.C.So, J. Zheng and K.W.K. Lui, (2008), “Best Linear Unbiased Estimator approach
for time-of –arrival based localization,” IET Signal Processing, vol.2, no.2,pp.156-162.
[32] J. C. Chen, R.E. Hudson and K. Yao, (2002), “Maximum-likelihood source localization and unknown
sensor location estimation for wideband signals in the near field,” IEEE Transactions on Signal
Processing, vol.50, no.8, pp.1843-1854.
[33] G. C. Carter, Ed.(1993), Coherence and Time Delay Estimation: An Applied Tutorial for Research,
Development, test, and Evaluation Engineers. New York: IEEE.
15. Signal & Image Processing : An International Journal (SIPIJ) Vol.6, No.1, February 2015
59
[34] M. Marks and E. Niewiadomska - Szynkiewicz, (2007), “Two-Phase Stochastic optimization to
sensor network localization,” Proc. IEEE International Conference on Sensor Technologies and
Applications, Valencia, Spain, pp. 134-139.
[35] Y. T. Chan and K.C. Ho, (1994) “A Simple and Efficient estimator for hyperbolic location,” IEEE
Transactions on Signal Processing, vol. 42, no. 8, pp. 1905 – 1915.
[36] H. C. So, Y. T. Chan and F.K.W. Chan, (2008), “Closed-form formulae for optimum time difference
of arrival based localization,” IEEE Transactions on Signal Processing, vol.56, no.6, pp. 2614-2620.
AUTHORS
Ravindra. S. received his B.E., in Electrical and Electronics Engineering., and
M.Tech., in Networking and Internet Engineering, from Visvesvaraya
Technological University, Belgaum, Karnataka, India in 2006 and 2008
respectively. He is currently working towards a Doctoral Degree from
Visvesvaraya Technological University, Belgaum, Karnataka, India. At present
he is working as Assistant Professor, in Computer Science and Engineering
department of Jawaharlal Nehru National College of Engineering (affiliated to
Visvesvaraya Technological University), Shimoga, Karnataka, India.
Dr. S.N. Jagadeesha received his B.E., in Electronics and Communication
Engineering, from University B. D. T College of Engineering., Davangere
affiliated to Mysore University, Karnataka, India in 1979, M.E. from Indian
Institute of Science (IISC), Bangalore, India specializing in Electrical
Communication Engineering., in 1987 and Ph.D. in Electronics and Computer
Engineering., from University of Roorkee, Roorkee, India in 1996. He is an
IEEE member. His research interest includes Array Signal Processing, Wireless
Sensor Networks and Mobile Communications. He has published and presented
many papers on Adaptive Array Signal Processing and Direction-of-Arrival
estimation. Currently he is professor in the department of Computer Science and
Engineering, Jawaharlal Nehru National College of Engg. (Affiliated to
Visvesvaraya Technological University), Shimoga, Karnataka, India.