This document presents a fault tolerant integrated navigation scheme using an adaptive federated Kalman filter. The scheme integrates strapdown inertial navigation system (SINS) measurements with GPS and celestial navigation system (CNS) measurements. It uses a federated Kalman filter architecture with two local filters (one for SINS/GPS and one for SINS/CNS) and a master filter. Weighting factors are introduced and adapted online to tune the contribution of each local filter in the final data fusion, making the scheme nearly optimal and fault tolerant. The measurement noise covariance is also made adaptive using a fuzzy inference system based on the relative degree of mismatch between actual and theoretical innovation covariances.
INS/GPS integrated navigation system is studied in this paper for the hypersonic UAV in order to
satisfy the precise guidance requirements of hypersonic UAV and in response to the defects while the
inertial navigation system (INS) and the global positioning system (GPS) are being applied separately. The
information of UAV including position, velocity and attitude can be obtained by using INS and GPS
respectively after generating a reference trajectory. The corresponding errors of two navigation systems
can be obtained through comparing the navigation information of the above two guidance systems.
Kalman filter is designed to estimate the navigation errors and then the navigation information of INS are
corrected. The non-equivalence relationship between the platform misalignment angle and attitude error
angle are considered so that the navigation accuracy is further improved. The Simulink simulation results
show that INS/GPS integrated navigation system can help to achieve higher accuracy and better antiinterference
ability than INS navigation system and this system can also satisfy the navigation accuracy
requirements of hypersonic UAV.
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
INS/GPS integrated navigation system is studied in this paper for the hypersonic UAV in order to
satisfy the precise guidance requirements of hypersonic UAV and in response to the defects while the
inertial navigation system (INS) and the global positioning system (GPS) are being applied separately. The
information of UAV including position, velocity and attitude can be obtained by using INS and GPS
respectively after generating a reference trajectory. The corresponding errors of two navigation systems
can be obtained through comparing the navigation information of the above two guidance systems.
Kalman filter is designed to estimate the navigation errors and then the navigation information of INS are
corrected. The non-equivalence relationship between the platform misalignment angle and attitude error
angle are considered so that the navigation accuracy is further improved. The Simulink simulation results
show that INS/GPS integrated navigation system can help to achieve higher accuracy and better antiinterference
ability than INS navigation system and this system can also satisfy the navigation accuracy
requirements of hypersonic UAV.
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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%.
GPS Instrumental Biases Estimation Using Continuous Operating Receivers NetworkCSCJournals
Precise Total Electron Content (TEC) are required to produce accurate spatial and temporal resolution of Global Ionosphere Maps (GIMs). Receivers and Satellites Instrumental Biases (IBs) are one of the main error sources in estimating precise TEC from Global Positioning Systems (GPS) data. Recently, researchers are interested in developing models and algorithms to compute IBs of receivers and satellites close to those computed from the Ionosphere Associated Analysis Centers (IAAC). Here we introduce a MATLAB code called Multi Station IBs Estimation (MSIBE) to calculate satellites and codeless tracking receivers IBs from GPS data. MSIBE based on spherical harmonic function and geometry free combination of GPS carrier phase and pseudo-range code observations and weighted least square were applied to solve observation equations, to improve estimation of IBs values. There are many factors affecting estimated value of IBs. The premier factor is the observations weighting function which relying on the satellite elevation angle. The second factor concerned with estimating IBs using single GPS Station Precise Point Positioning (PPP) or using GPS network. The third factor is the number of GPS receivers in the network. Results from MSIBE were evaluated and compared with data from IAAC and other codes like M_DCB and ZDDCBE. The results of weighted (MSIBE) least square shows an improvement for estimated IBs, where mean differences from CODE less than 0.746 ns. IBs estimated from Continuous Operating Receivers (CORs) GPS network shows a good agreement with IAAC than IBs estimated from PPP where the mean differences are less than 0.1477 ns and 1.1866 ns, respectively. The mean differences of computed IBs improved by increasing number of GPS stations in the network.
Process RTK Data Based On Kalman Filtering AlgorithmIJRES Journal
With the development of satellite positioning technology, there is a strong need for high accuracy
position information. Currently the most widely used high-precision positioning technology is RTK(Real-Time
Kinematic).RTK technology is the key to using carrier phase measurements. It takes advantage of the base
stations and monitor stations observed error of spatial correlation, except monitor stations observed by means of
differential most of the errors in the data, in order to achieve high accuracy positioning.[3]Based on Kalman
filtering algorithm to handle the noise of RTK data and selecting appropriate models to further improve the
accuracy of the data. This paper will explore the use of Kalman filtering method of RTK data processing, which
reduces random noise interference, thus improving the accuracy of GNSS deformation monitoring data.[1]
Estimation of global solar radiation by using machine learning methodsmehmet şahin
In this study, global solar radiation (GSR) was estimated based on 53 locations by using ELM, SVR, KNN, LR and NU-SVR methods. Methods were trained with a two-year data set and accuracy of the mentioned methods was tested with a one-year data set. The data set of each year was consisting of 12 months. Whereas the values of month, altitude, latitude, longitude, vapour pressure deficit and land surface temperature were used as input for developing models, GSR was obtained as output. Values of vapour pressure deficit and land surface temperature were taken from radiometry of NOAA-AVHRR satellite. Estimated solar radiation data were compared with actual data that were obtained from meteorological stations. According to statistical results, most successful method was NU-SVR method. The RMSE and MBE values of NU-SVR method were found to be 1,4972 MJ/m2 and 0,2652 MJ/m2, respectively. R value was 0,9728. Furthermore, worst prediction method was LR. For other methods, RMSE values were changing between 1,7746 MJ/m2 and 2,4546 MJ/m2. It can be seen from the statistical results that ELM, SVR, k-NN and NU-SVR methods can be used for estimation of GSR.
Analysis of Time Diversity Gain for Satellite Communication Link based on Ku-...IJECEIAES
This paper reports a study on mitigation of propagation impairments on Earth–space communication links. The study uses time diversity as a technique for mitigating rain propagation impairment in order to rectify rain fade. Rain attenuation time series along earth-to-satellite link were measured for two years period at 12.255 GHz in Malaysia. The time diversity technique was applied on measured rain fade to investigate the level of possible improvement in system. Time diversity gain from measured oneminute rain attenuation for two years period was estimated and significant improvement was observed with different delays of time. These findings will be utilized as a useful tool for link designers to apply time diversity as a rain fade mitigation technique in Earth-satellite communications systems.
DUAL BAND GNSS ANTENNA PHASE CENTER CHARACTERIZATION FOR AUTOMOTIVE APPLICATIONSjantjournal
High-accuracy Global Navigation Satellite System (GNSS) positioning is a prospective technology that will be used in future automotive navigation systems. This system will be a composite of the United States' Global Positioning System (GPS), the Russian Federation's Global Orbiting Navigation Satellite System (GLONASS), China Beidou Navigation Satellite System (BDS) and the European Union’s Galileo. The major improvement in accuracy and precision is based on (1) multiband signal transmitting, (2) carrier phase correction, (3) Real Time Kinematic (RTK). Due to the size and high-cost of today’s survey-grade antenna solutions, this kind of technology is difficult to use widely in the automotive sector. In this paper, a low-cost small size dual-band ceramic GNSS patch antenna is presented from design to real sample. A further study of this patch antenna illustrates the absolute phase center variation measured in an indoor range to achieve a received signal phase error correction. In addition, this low-cost antenna solution is investigated when integrated into a standard multi-band automotive antenna product. This product is evaluated both on its own in an indoor range and on a typical vehicle roof at an outdoor range. By using this evaluation file to estimate the receiver position could achieve phase motion error-free result.
BLE-Based Accurate Indoor Location Tracking for Home and Office csandit
Nowadays the use of smart mobile devices and the accompanying needs for emerging services
relying on indoor location-based services (LBS) for mobile devices are rapidly increasing. For
more accurate location tracking using Bluetooth Low Energy (BLE), this paper proposes a
novel trilateration-based algorithm and presents experimental results that demonstrate its
effectiveness.
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.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Refining Underwater Target Localization and Tracking EstimatesCSCJournals
Improving the accuracy and reliability of the localization estimates and tracking of underwater targets is a constant quest in ocean surveillance operations. The localization estimates may vary owing to various noises and interferences such as sensor errors and environmental noises. Even though adaptive filters like the Kalman filter subdue these problems and yield dependable results, targets that undergo maneuvering can cause incomprehensible errors, unless suitable corrective measures are implemented. Simulation studies on improving the localization and tracking estimates for a stationary target as well as a moving target including the maneuvering situations are presented in this paper
Hybrid hmmdtw based speech recognition with kernel adaptive filtering methodijcsa
We have proposed new approach for the speech recognition system by applying kernel adaptive filter for
speech enhancement and for the recognition, the hybrid HMM/DTW methods are used in this paper. Noise
removal is very important in many applications like telephone conversation, speech recognition, etc. In the
recent past, the kernel methods are showing good results for speech processing applications. The feature
used in the recognition process is MFCC features. It consists of a HMM system used to train the speech
features and for classification purpose used the DTW method. Experimental results show a relative
improvement of recognition rate compared to the traditional methods.
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%.
GPS Instrumental Biases Estimation Using Continuous Operating Receivers NetworkCSCJournals
Precise Total Electron Content (TEC) are required to produce accurate spatial and temporal resolution of Global Ionosphere Maps (GIMs). Receivers and Satellites Instrumental Biases (IBs) are one of the main error sources in estimating precise TEC from Global Positioning Systems (GPS) data. Recently, researchers are interested in developing models and algorithms to compute IBs of receivers and satellites close to those computed from the Ionosphere Associated Analysis Centers (IAAC). Here we introduce a MATLAB code called Multi Station IBs Estimation (MSIBE) to calculate satellites and codeless tracking receivers IBs from GPS data. MSIBE based on spherical harmonic function and geometry free combination of GPS carrier phase and pseudo-range code observations and weighted least square were applied to solve observation equations, to improve estimation of IBs values. There are many factors affecting estimated value of IBs. The premier factor is the observations weighting function which relying on the satellite elevation angle. The second factor concerned with estimating IBs using single GPS Station Precise Point Positioning (PPP) or using GPS network. The third factor is the number of GPS receivers in the network. Results from MSIBE were evaluated and compared with data from IAAC and other codes like M_DCB and ZDDCBE. The results of weighted (MSIBE) least square shows an improvement for estimated IBs, where mean differences from CODE less than 0.746 ns. IBs estimated from Continuous Operating Receivers (CORs) GPS network shows a good agreement with IAAC than IBs estimated from PPP where the mean differences are less than 0.1477 ns and 1.1866 ns, respectively. The mean differences of computed IBs improved by increasing number of GPS stations in the network.
Process RTK Data Based On Kalman Filtering AlgorithmIJRES Journal
With the development of satellite positioning technology, there is a strong need for high accuracy
position information. Currently the most widely used high-precision positioning technology is RTK(Real-Time
Kinematic).RTK technology is the key to using carrier phase measurements. It takes advantage of the base
stations and monitor stations observed error of spatial correlation, except monitor stations observed by means of
differential most of the errors in the data, in order to achieve high accuracy positioning.[3]Based on Kalman
filtering algorithm to handle the noise of RTK data and selecting appropriate models to further improve the
accuracy of the data. This paper will explore the use of Kalman filtering method of RTK data processing, which
reduces random noise interference, thus improving the accuracy of GNSS deformation monitoring data.[1]
Estimation of global solar radiation by using machine learning methodsmehmet şahin
In this study, global solar radiation (GSR) was estimated based on 53 locations by using ELM, SVR, KNN, LR and NU-SVR methods. Methods were trained with a two-year data set and accuracy of the mentioned methods was tested with a one-year data set. The data set of each year was consisting of 12 months. Whereas the values of month, altitude, latitude, longitude, vapour pressure deficit and land surface temperature were used as input for developing models, GSR was obtained as output. Values of vapour pressure deficit and land surface temperature were taken from radiometry of NOAA-AVHRR satellite. Estimated solar radiation data were compared with actual data that were obtained from meteorological stations. According to statistical results, most successful method was NU-SVR method. The RMSE and MBE values of NU-SVR method were found to be 1,4972 MJ/m2 and 0,2652 MJ/m2, respectively. R value was 0,9728. Furthermore, worst prediction method was LR. For other methods, RMSE values were changing between 1,7746 MJ/m2 and 2,4546 MJ/m2. It can be seen from the statistical results that ELM, SVR, k-NN and NU-SVR methods can be used for estimation of GSR.
Analysis of Time Diversity Gain for Satellite Communication Link based on Ku-...IJECEIAES
This paper reports a study on mitigation of propagation impairments on Earth–space communication links. The study uses time diversity as a technique for mitigating rain propagation impairment in order to rectify rain fade. Rain attenuation time series along earth-to-satellite link were measured for two years period at 12.255 GHz in Malaysia. The time diversity technique was applied on measured rain fade to investigate the level of possible improvement in system. Time diversity gain from measured oneminute rain attenuation for two years period was estimated and significant improvement was observed with different delays of time. These findings will be utilized as a useful tool for link designers to apply time diversity as a rain fade mitigation technique in Earth-satellite communications systems.
DUAL BAND GNSS ANTENNA PHASE CENTER CHARACTERIZATION FOR AUTOMOTIVE APPLICATIONSjantjournal
High-accuracy Global Navigation Satellite System (GNSS) positioning is a prospective technology that will be used in future automotive navigation systems. This system will be a composite of the United States' Global Positioning System (GPS), the Russian Federation's Global Orbiting Navigation Satellite System (GLONASS), China Beidou Navigation Satellite System (BDS) and the European Union’s Galileo. The major improvement in accuracy and precision is based on (1) multiband signal transmitting, (2) carrier phase correction, (3) Real Time Kinematic (RTK). Due to the size and high-cost of today’s survey-grade antenna solutions, this kind of technology is difficult to use widely in the automotive sector. In this paper, a low-cost small size dual-band ceramic GNSS patch antenna is presented from design to real sample. A further study of this patch antenna illustrates the absolute phase center variation measured in an indoor range to achieve a received signal phase error correction. In addition, this low-cost antenna solution is investigated when integrated into a standard multi-band automotive antenna product. This product is evaluated both on its own in an indoor range and on a typical vehicle roof at an outdoor range. By using this evaluation file to estimate the receiver position could achieve phase motion error-free result.
BLE-Based Accurate Indoor Location Tracking for Home and Office csandit
Nowadays the use of smart mobile devices and the accompanying needs for emerging services
relying on indoor location-based services (LBS) for mobile devices are rapidly increasing. For
more accurate location tracking using Bluetooth Low Energy (BLE), this paper proposes a
novel trilateration-based algorithm and presents experimental results that demonstrate its
effectiveness.
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.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Refining Underwater Target Localization and Tracking EstimatesCSCJournals
Improving the accuracy and reliability of the localization estimates and tracking of underwater targets is a constant quest in ocean surveillance operations. The localization estimates may vary owing to various noises and interferences such as sensor errors and environmental noises. Even though adaptive filters like the Kalman filter subdue these problems and yield dependable results, targets that undergo maneuvering can cause incomprehensible errors, unless suitable corrective measures are implemented. Simulation studies on improving the localization and tracking estimates for a stationary target as well as a moving target including the maneuvering situations are presented in this paper
Hybrid hmmdtw based speech recognition with kernel adaptive filtering methodijcsa
We have proposed new approach for the speech recognition system by applying kernel adaptive filter for
speech enhancement and for the recognition, the hybrid HMM/DTW methods are used in this paper. Noise
removal is very important in many applications like telephone conversation, speech recognition, etc. In the
recent past, the kernel methods are showing good results for speech processing applications. The feature
used in the recognition process is MFCC features. It consists of a HMM system used to train the speech
features and for classification purpose used the DTW method. Experimental results show a relative
improvement of recognition rate compared to the traditional methods.
Classical Discrete-Time Fourier TransformBased Channel Estimation for MIMO-OF...IJCSEA Journal
In this document, we look at various time domain channel estimation methods with this constraint of null carriers at spectrumborders.We showin detail howto gauge the importance of the “border effect” depending on the number of null carriers, which may vary from one system to another. Thereby we assess the limit of the technique discussed when the number of null carriers is large. Finally the DFT with the truncated singular value decomposition (SVD) technique is proposed to completely eliminate the impact of the null subcarriers whatever their number. A technique for the determination of the truncation threshold for any MIMO-OFDM system is also proposed.
IMPROVEMENT OF LTE DOWNLINK SYSTEM PERFORMANCES USING THE LAGRANGE POLYNOMIAL...IJCNCJournal
To achieve a high speed data rate, higher spectral efficiency, improved services and low latency the 3rd
generation partnership project designed LTE standard (Long Term Evolution).the LTE system employs
specific technical as well the technical HARQ, MIMO transmission, OFDM Access or estimation technical.
In this paper we focus our study on downlink LTE channel estimation and specially the interpolation which
is the basis of the estimation of the channel coefficients. Thus, we propose an adaptive method for polynomial interpolation based on Lagrange polynomial. We perform the Downlink LTE system MIMO transmission then compare the obtained results with linear, Sinus Cardinal and polynomial Newton Interpolations. The simulation results show that the Lagrange method outperforms system performance in term of Block Error Rate (BLER) , throughput and EVN(%)vs. Signal to Noise Ratio (SNR).
An improved fading Kalman filter in the application of BDS dynamic positioningIJRES Journal
Aiming at the poor dynamic performance and low navigation precision of traditional fading
Kalman filter in BDS dynamic positioning, an improved fading Kalman filter based on fading factor vector is
proposed. The fading factor is extended to a fading factor vector, and each element of the vector corresponds to
each state component. Based on the difference between the actual observed quantity and the predicted one, the
value of the vector is changed automatically. The memory length of different channel is changed in real time
according to the dynamic property of the corresponding state component. The actual observation data of BDS is
used to test the algorithm. The experimental results show that compared with the traditional fading Kalman filter
and the method of the third references, the positioning precision of the algorithm is improved by 46.3% and
23.6% respectively.
Modified Adaptive Lifting Structure Of CDF 9/7 Wavelet With Spiht For Lossy I...idescitation
We present a modified structure of 2-D cdf 9/7 wavelet
transforms based on adaptive lifting in image coding. Instead
of alternately applying horizontal and vertical lifting, as in
present practice, Adaptive lifting performs lifting-based
prediction in local windows in the direction of high pixel
correlation. Hence, it adapts far better to the image orientation
features in local windows. The predicting and updating signals
of Adaptive lifting can be derived even at the fractional pixel
precision level to achieve high resolution, while still
maintaining perfect reconstruction. To enhance the
performance of adaptive based modified structure of 2-D CDF
9/7 is coupled with SPIHT coding algorithm to improve the
drawbacks of wavelet transform. Experimental results show
that the proposed modified scheme based image coding
technique outperforms JPEG 2000 in both PSNR and visual
quality, with the improvement up to 6.0 dB than existing
structure on images with rich orientation features .
RSSI based localization techniques are effected by environmental factors which cause the RF
signalsemitted from transmitter nodes fluctuate in time domain. These variations generate fluctuations on
distance calculations and result false object position detection during localization.Smoothing procedures
must be applied on distance values either collectively or individually to minimize these fluctuations. In this
study,proposed detection system has two main phases. Firstly, calibration of RSSI values with respect to
distances and calculation of environmental coefficient for each transmitter.Secondly, position estimation of
objects by applyingiterative trilateration on smoothed distance values. A smoothing algorithm is employed
to minimize the dynamic fluctuations of RF signals received from each reference transmitter node.
Distances between the reference nodes and the objects are calculated by deploying environmental
coefficients. Experimental measurements are carried out to measure the sensitivity of the system. Results
show that the proposed system can be deployed as a viable position detection system in indoors and
outdoors.
Mitigating Interference to GPS Operation Using Variable Forgetting Factor Bas...IJCNCJournal
In this paper, an interference method based on signal processing is proposed. The approach is based on
utilizing the maximum likelihood properties of the received signal. The approach is built on maximizing the
probability of the desired data. The GPS data, which is constructed using Binary Phase Shift Keying
(BPSK) modulation, is transmitted as “1’s” and as “0’s.” carried on 1575.42MHz carrier called the L1
frequency. The statistics of the GPS data and interference are utilized in terms of their distribution and
variance. The statistics are used to update (adaptively) the forgetting factor (Lambda) of the Recursive
Least Squares (RLS) filter. The proposed method is called Maximum Likelihood Variable Forgetting Factor
(ML VFF). The adaptive update takes on assigning lambda to the maximum of the probabilities of the
symbols based on the statistics mentioned.
ENVIRONMENTALLY CORRECTED RSSI BASED REAL TIME LOCATION DETECTION SYSTEMijcsit
RSSI based localization techniques are effected by environmental factors which cause the RF signalsemitted from transmitter nodes fluctuate in time domain. These variations generate fluctuations on distance calculations and result false object position detection during localization.Smoothing procedures must be applied on distance values either collectively or individually to minimize these fluctuations. In this
study,proposed detection system has two main phases. Firstly, calibration of RSSI values with respect to distances and calculation of environmental coefficient for each ransmitter.Secondly, position estimation of objects by applyingiterative trilateration on smoothed distance values. A smoothing algorithm is employed to minimize the dynamic fluctuations of RF signals received from each reference transmitter node. Distances between the reference nodes and the objects are calculated by deploying environmental
coefficients. Experimental measurements are carried out to measure the sensitivity of the system. Results show that the proposed system can be deployed as a viable position detection system in indoors and outdoors.
A ROS IMPLEMENTATION OF THE MONO-SLAM ALGORITHMcsandit
Computer vision approaches are increasingly used in mobile robotic systems, since they allow
to obtain a very good representation of the environment by using low-power and cheap sensors.
In particular it has been shown that they can compete with standard solutions based on laser
range scanners when dealing with the problem of simultaneous localization and mapping
(SLAM), where the robot has to explore an unknown environment while building a map of it and
localizing in the same map. We present a package for simultaneous localization and mapping in
ROS (Robot Operating System) using a monocular camera sensor only. Experimental results in
real scenarios as well as on standard datasets show that the algorithm is able to track the
trajectory of the robot and build a consistent map of small environments, while running in near
real-time on a standard PC.
Time domain analysis and synthesis using Pth norm filter designCSCJournals
In this paper, a new approach for the design and implementation of FIR filter banks for multirate analysis and synthesis is explored. The method is based on the least algorithm and takes into consideration the characteristics of the individual filters. Features of the proposed approach include; it does not need to adapt the weighting function involved and no constraints are imposed during the course of optimization. Mostly, the FIR filter design is concentrated around linear phase characteristics but with the help of minimax solution for FIR filters using the least- algorithm, this optimal filter design approach helps us to enhance the properties of LTI systems with better stability filter coefficient convergence. Hence norm algorithm will be used in multirate to explore the stability and other properties. We have proposed the band analysis system for analysis and synthesis purpose to explore multirate filter banks. The Matlab toolbox has been used for implementing the filters and its properties will be verified with various plots and tables. The results of this paper enable us to achieve good signal to noise ratio with analysis and synthesis level operations.
Adaptive Channel Equalization for Nonlinear Channels using Signed Regressor F...IDES Editor
Wireless communication systems are affected by
inter-symbol interference (ISI), co-channel interference in
the presence of additive white Gaussian noise. ISI is primarily
due to the distortion caused by frequency and time selectivity
of the fading channel and it causes performance degradation.
Equalization techniques are used to mitigate the effect of ISI
and noise for better demodulation. This paper presents a novel
technique for channel equalization. Here a Signed Regressor
adaptive algorithm based on FLANN (Functional Link Artificial
Neural Network) has been developed for nonlinear channel
equalization along with the analysis of MSE and BER. The
results are compared with the conventional adaptive LMS
algorithm based FLANN model. The Signed Regressor FLANN
shows better performance as compared to LMS based FLANN.
The equalizer presented shows considerable performance
compared to the other adaptive structure for both the linear
and non-linear models in terms of convergence rate, MSE
and BER over a wide range.
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Similar to 1-A Fault Tolerant Integrated Navigation Scheme Realized through Online Tuning of Weighting Factors for Federated Kalman Filter (20)
1-A Fault Tolerant Integrated Navigation Scheme Realized through Online Tuning of Weighting Factors for Federated Kalman Filter
1. A Fault Tolerant Integrated Navigation Scheme Realized through Online
Tuning of Weighting Factors for Federated Kalman Filter
Muhammad Ushaq1,a*
and Fang Jian Cheng2,b
a
PhD Candidate, b
Professor
1,2
School of Instrumentation Science & Optoelectronics Engg, Beihang University, 37 Xue
Yuan Lu Haidian District Beijing, People Republic of China
*
ushaq71@yahoo.com (Phone: 0086-15011530142)
Keywords: Adaptive Kalman Filter, Integrated Navigation, Data Fusion, Federated Filter, Fault
Detection and Isolation
Abstract: Strapdown Inertial navigation (SINS) is a highly reliable navigation system for short
term applications. SINS functions continuously, less hardware failures, renders high speed
navigation solutions ranging from 50 Hz to 1000 Hz and exhibits low short-term errors. It provides
efficient attitude, angular rate, acceleration, velocity and position solutions. But, the accuracy of
SINS solution vitiates with time as the sensor (gyros & accelerometers) errors are integrated
through the navigation equations. Average navigation grade SINS are capable of providing effective
stand-alone navigation for shorter duration (few minutes) applications Stand-alone SINS capable of
providing solutions for applications exceeding 10 minutes duration, are generally highly expensive
($0.1M to $2.0M). To cope with this limitation, a cost effective solution is the integrated navigation
system wherein the unboundedly growing errors of SINS are contained with the help of external
non-inertial navigation aids like GPS, Celestial Navigation System (CNS), Odometer, Doppler
radars etc. The efficient methodology for integrated or multi-sensory navigation is the Federated
Kalman Filter (FKF) scheme. In FKF architecture, a reference SINS solution is integrated
independently with each of the aiding navigation systems in a bank of local Kalman filters. There
are a number of different ways in which the local filter outputs may be combined to produce an
integrated navigation solution. The no-reset, fusion-reset, zero-reset, and cascaded versions of
federated integration have been used by different researcher and navigators over the years. All
different schemes of FKF have certain pros and cons. Fusion-reset method although nearly optimal
is less fault tolerant while no-resent scheme renders highly fault tolerant solutions but with sub-
optimal solutions and compromised precision. To enhance the fault tolerance ability of fusion-reset
scheme of FKF, additional parameters called weighting factors are introduced to tune the
contribution of each local filter in the final data fusion. The presented scheme has been found nearly
optimal and expressively fault tolerant.
1. Introduction
Navigation system purely based on SINS renders erroneous solution if employed for longer
duration missions, as inertial navigation system errors are accumulated unboundedly over the time.
Integrated navigation is the remedy for this limitation of SINS[1–3]. Kalman filter algorithm is
invariably used for integration of SINS with other non-inertial navigation aids. The key function
performed by the Kalman filter is the statistical combination of navigation aids and SINS
information to track drifting parameters of the gyros and accelerometer used in SINS. As a result,
the SINS can provide navigation solutions with enhanced precision during periods even when
aiding signals may be lost, and the improved position and velocity estimates from the SINS can
then be used to make aiding signal reacquisition, happen much faster when the those signal
becomes available again[4]. In this research work GPS and CNS are used as external navigation
aids to contain the drifting behavior of inertial sensors used in SINS. The scheme is implemented
on a simulated trajectory of an aircraft for duration of 2300s. In a standard centralized Kalman
Filter (CKF) integration architecture, the systematic errors and noise sources of all of the navigation
sensors are modeled in one Kalman filter. This ensures that all error correlations are accounted for,
2. all measurements optimally weighted, and the maximum information used to calibrate each error.
CKF architecture provides the optimal navigation solution in terms of accuracy. With all of the
error sources modeled in one place, the principal disadvantage of CKF is a high processor load.
With no independent subsystem navigation solutions available, processor-intensive parallel filters
are required for applications with fault-tolerant requirements. FKF avoids the theoretical and
practical limitations of CKF. This is realized by means of information sharing methodology[5][6].
The observations from the different aiding sensors are generally independent of each other therefore
any error in one sensor will not contaminate the solution from other sensor. This particular feature
can be observed from Figure 6 where attitude solutions provided by SINS/CNS filter are not
affected by errors in velocity and position measurements from GPS; furthermore, the redundant
observations can be used to detect or accommodate the outliers. Moreover, the throughput is
significantly improved due to the parallel processing. Each local filter is dedicated to a separate
sensor subsystem, and also uses data from the common reference SINS. The SINS acts as a central
sensor in the architecture, and its data is the measurement input for the master filter. In this research
work, the data from the CNS and GPS is dedicated to the local KF-1 and KF-2 respectively as
shown in Figure 1.
2. Fault Tolerant Federated Kalman Filtering Architecture
The presented integrated navigation architecture realized through FKF is shown in Figure 1. As
elucidated each KF is dedicated to a distinct sensor subsystem, and also uses data from the central
system SINS. The data from the GPS and CNS is dedicated to the corresponding KFs which after
implementation provide their solutions to the MF for the master update, yielding a global solution
[6], [7]. Moreover a fault detection and adaption of statistical features has been implemented for
each local filter.
Master
Filter
SINS
GPS
CNS
Adaption
of
R
KF-1
For
SINS/GPS
KF-2
For
SINS/CNS
Time
Update
Measurement
Update
1
1
ˆ ,f fx P
1
2
ˆ ,f fx P
1 1
ˆ ,x P
2 2
ˆ ,x P
Corrected Solution for Position, Velocity and Attitude
+
-
+
-
Adaption
of
R
Observation For KF-2
Observation For KF-1
Computation
of Weighting
Factors
&
Final Data
Fusion
Corrections
Figure 1 SINS/GPS/CNS Navigation Scheme Based on FKF
Time update equations for the state vector and the state error covariance for LFs and MF are:
, | 1 , , 1
ˆ ˆ ,i k k i k i kx x , 1 1
, | 1 , 1 ,[ ] , 1,2, ,T
i k k k i k k i kP P Q i n
(1)
Kalman gain for each local filter is computed as:
3. 1
/ 1 / 1 1( )T T
k k k k k k k k kK P H H P H R
(2)
Measurement update equations for the LFs are:
1 1 1
, , | 1 ,
T
i k i k k i i k iP P H R H
, 1 1
, , , | 1 , | 1 , ,
ˆ ˆ , 1,2, ,T
i k i k i k k i k k i i k i kx P P x H R z i n
(3)
Fusion update is performed as follows:
1 1
, ,
1
l
f k i k
i
P P
,
1
, , , ,
1
ˆ ˆ
l
f k f k i k i k
i
x P P x
(4)
Where 1
,
f fn n
f kP
is the inverse of the fused covariance, ,
ˆ fn
f kx is the fused state estimate.
In order to suppress the effect of correlation, the process noise and the state vector covariance are
set to their upper bounds as per Eqs (5).
1
, ,i k i kQ Q
,
1
, , , , 1,2, ,i k i k f kP P i n
(5)
Where ( 0)i are information sharing coefficients satisfying the following condition[5]:
1 2 1n (6)
When the fusion reset mode is realized, the covariance matrices of LFs and MF are reset by using
Eqs (5). The state estimates of the LFs are also reset by the fused solution as shown in Eq (7)
, ,
ˆ ˆ , 1,2,i k f kx x i n (7)
The measurement noise covariance matrix kR is made adaptive using following equation
ˆ
k kR R (8)
is computed using Fuzzy Inference System (FIS) taking relative degree of mismatch (RDOM)
given byEq (9) as input function and as output function.
1
/ 1 1
1
ˆ ˆ
k
T
k k k k k k
i k M
i T
k k k k k
trace z H x z H x
M
RDOM
trace H P H R
(9)
In equation
1
1
ˆ ˆ
k T
k k k k k k
i k M
z H x z H x
M
the actual innovation covariance and
/ 1 1
T
k k k k kH P H R is the theoretical innovation covariance[8], [9].
3. Measurement Model for Local Filters
The measurement models for local filters, SINS/GPS and SINS/CNS are represented by the
corresponding measurement matrices 1H and 2H respectively are given as follows[10], [11]:
1
0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 Rm h 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 Rn h cos lat 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
H
(10)
4. Whereas: 2
1 2 3( )M eR R e Sine and 2
R 1( )N eR Sine
2 3 12
3 15
0
0 0
0 1
X
X
Cos Cos Sin
H Sin Cos Cos
Sin
(11)
In Eq (11) and are heading and pitch of the vehicle respectively.
4. Weighted and Scaled Data Fusion
The conventional scheme for detection and isolation of faults in local filters is a 2
test wherein a
test statistic
1
ˆ ˆ( )
T T
i k k k k k k k kk z H x HP H R z H x
, a scalar having 2
distribution is
tested against a determined threshold. If ( )i k is found exceeding the threshold the measurement
update for that particular epoch is skipped. But here in this work we have introduced the scaled
weighting factors, i which adjust the weights or contributing factor for each local filter estimates
and fused estimates. Finally based on all weighting factors a global solution is obtained. The
deriving variable in computation of weighting factors is the iRDOM . For each value of iRDOM a
corresponding confidence factor is computed as shown in Figure 2. is equal to 1 for a range of
0.7 1.3iRDOM indicating normal operation, value of grows linearly for range
0.1 0.7iRDOM while decreases linearly for 1.3 10iRDOM and finally 0 for
0.1 , 10i iRDOM RDOM . These computation of Vs RDOM can be made using FIS or
simple piecewise linear & constant function. Here application of probability set theory is made to
explain the relative confidence factors and weighting factors related with individual local filters and
the fused filter using FKF. Let 1C , 2C and 3C are the subsets of confidence for which the local
filters SINS/GPS (KF-1), SINS/CNS and FKF are effectual respectively[12]. It may be noted that
3C is the subset of confidence for which the data fusion using FKF is valid. Another subset 0C can
be defined as the subset of confidence for which the solutions from both local filters (KF-1 and KF-
2) becomes ineffectual. These subsets of confidence can be defined by following Eqs
1 1( )P C , 2 2( )P C and 3 1 2 1 2( ) ( )P C P C C
Let us define a confidence space as follows:
1 1 1, , 0.1 10iS C C C RDOM (12)
In the light of above-mentioned subsets of confidence and the overall confidence space we can
compute the Probability of validity or relative weighting factor corresponding to each local filter as
in following Eqs.
Weighting factor for SINS/GPS validity:
1 1 1 2 1 1 2( ) ( ) ( )P C P C P C (13)
Weighting factor for SINS/CNS validity:
2 2 1 2 2 1 2( ) ( ) ( )P C P C P C (14)
Weighting factor for SINS/SNS/GPS Integration Using FKF validity (or when both local filters are
valid hence FKF based data fusion is valid):
3 1 2 1 2( ) ( )P C P C (15)
5. Finally Weighting factor for the probability that both SINS/GPS and SINS/CNS local filters are
invalid hence data fusion based FKF is also invalid given by
0 1 2 1 2 31 ( ) 1P C C (16)
Having computed all of weighting factors final data fusion at epoch k is done as follows:
Time update of state vector and state vector covariance is given as:
| 1 1
ˆ ˆk k k kx x (17)
| 1 1
T T
k k k k k kP P Q (18)
The measurement update for the presented scheme is given as follows:
0 / 1 1 1, 2 2, 3 ,
ˆ ˆ ˆ ˆ ˆk k k k k f kx x x x x (19)
0 / 1 1 1, 1, 1, 2 2, 2, 2,
3 , , ,
ˆ ˆ ˆ ˆ ˆ ˆ ˆ ˆ
ˆ ˆ ˆ ˆ
T T
k k k k k k k k k k k k k
T
f k k f k k f k
P P P x x x x P x x x x
P x x x x
(20)
Figure 2 RDOMiVs Confidence Factor (η)
Time update to be used for next epoch is computed as:
1| 1
ˆ ˆk k k kx x (21)
1| 1 1
T T
k k k k k kP P Q (22)
It may be noted that if the confidence factor of SINS/GPS or SINS/CNS filter of FKS is 1, it makes
0 0 meaning that time update prediction component in Eq (19) is zero or time update is not used
in final data fusion. Otherwise if confidence factors of SINS/GPS and SINS/CNS both are 1, it
makes 0 1 2, and all equal to 0 and 3 1 meaning that in this case final data fusion fall back
to simple FKF given by Eq (4) or:
, ,
ˆ ˆ ,k f k k f kx x P P (23)
0 1 2 3 4 5 6 7 8 9 10
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
ConfidenceFactor()
RDOM
i
(0.1,0)
(0.7,1)
(1.3,1)
6. If the confidence factor of SINS/GPS and SINS/CNS are less than 1, then 0 0 meaning that the
contribution of time update in the final data fusion will be existent, and solutions of KF-1,KF-2 and
FKF will be scaled accordingly. If the confidence factors of both local filters are equal to 0 making
0 1 , meaning that in the final data fusion step, only time update will be used and the
measurement update and hence FKF fusion will be skipped.
5. Simulation and Results
To validate the performance of the scheme presented in this paper, a variation of simulations have
been performed employing CKF, standard FKF and FKF with weighted factors etc. The fault
tolerant strength has been tested by introducing deliberate errors of different types in the
measurement of aiding sensors at different time segments. For brevity of the paper the simulation
results for excessive random errors in GPS Position and Velocity measurement (10 times the
standard deviations) during time segment of 1800 to 1900s, are shown in Figure 4 to Figure 6. It
can be seen that the presented scheme has efficiently contained the errors as compared to standard
FKF scheme. The simulation results indicate that the excessive random error in KF-1 do not affect
the attitude solutions provided by KF-2.
Figure 3 Trajectory of the Vehicle
Figure 4 Velocity Errors
40
40.1
40.2
40.3
40.4
40.5
40.6
40.7
40.8
40.9
114.8
115
115.2
115.4
115.6
115.8
116
116.2
116.4
-0.5
0
0.5
1
1.5
2
2.5
3
x 10
4
Latitude (deg)
Trajectory
Longitude (deg)
Altitude(m)
Actual Trajectory
Estimated Trajectory
0 500 1000 1500 2000 2500
-0.03
-0.02
-0.01
0
0.01
Velocity Error (m/s)
VE
[m/s]
Standard FKF with Errors
FKF With Weighting Factor with Errors
Standard FKF Without Errors
0 500 1000 1500 2000 2500
-0.03
-0.02
-0.01
0
0.01
0.02
VN
[m/s]
Standard FKF with Errors
FKF With Weighting Factor with Errors
Standard FKF Without Errors
0 500 1000 1500 2000 2500
-0.02
0
0.02
0.04
0.06
0.08
VU
[m/s]
Time [sec]
Standard FKF with Errors
FKF With Weighting Factor with Errors
Standard FKF Without Errors
7. Figure 5 Position Errors
Figure 6 Attitude Errors
6. Conclusions
A fault tolerant integrated navigation algorithm based on FKF is presented in this paper. The
scheme employs a fault tolerant federated filtering architecture and combines multiple sensors
providing navigation information, including SINS, GPS and CNS. A scheme for online fault
detection, isolation and accommodation in implemented through tuning/adapting of weighting
factors of local filters. To adapt the measurement noise covariance of various contributing sensors
the innovation/residual based adaptive scheme has also been employed. The presented scheme
0 500 1000 1500 2000 2500
-50
0
50
100
Position Error (m)(Lat)[m]
Standard FKF with Errors
FKF With Weighting Factor with Errors
Standard FKF Without Errors
0 500 1000 1500 2000 2500
-100
-50
0
50
100
(Long)[m]
Standard FKF with Errors
FKF With Weighting Factor with Errors
Standard FKF Without Errors
0 500 1000 1500 2000 2500
-2
-1
0
1
2
3
4
(H)[m]
Time [sec]
Standard FKF with Errors
FKF With Weighting Factor with Errors
Standard FKF Without Errors
0 500 1000 1500 2000 2500
-100
0
100
200
300
400
Attitude Error (Arc Sec)
z
[]
Standard FKF with Errors
FKF With Weighting Factor with Errors
Standard FKF Without Errors
0 500 1000 1500 2000 2500
-400
-300
-200
-100
0
100
x
[]
Standard FKF with Errors
FKF With Weighting Factor with Errors
Standard FKF Without Errors
0 500 1000 1500 2000 2500
-100
0
100
200
300
400
y
[]
Time [sec]
Standard FKF with Errors
FKF With Weighting Factor with Errors
Standard FKF Without Errors
8. enables the system to detect and isolate the excessive random or step faults at sensor level. A
variety of simulations have been carried out to evaluate the performance of the presented scheme.
The presented scheme can be extended to any number of redundant/aiding navigation sensors. The
scheme can be applied to UAV or aircraft navigation systems efficiently and effectively.
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