The document presents an analysis of inertial navigation system (INS) positioning error caused by white Gaussian noise in IMU sensors. It derives the root mean squared error (RMSE) of INS position over time based on noise characteristics of gyroscopes and accelerometers. Monte Carlo simulations are performed and shown to closely match the derived RMSE as well as the Cramér-Rao lower bound (CRLB), demonstrating the accuracy of the proposed methodology.
Inertial Navigation for Quadrotor Using Kalman Filter with Drift Compensation IJECEIAES
The main disadvantage of an Inertial Navigation System is a low accuracy due to noise, bias, and drift error in the inertial sensor. This research aims to develop the accelerometer and gyroscope sensor for quadrotor navigation system, bias compensation, and Zero Velocity Compensation (ZVC). Kalman Filter is designed to reduce the noise on the sensor while bias compensation and ZVC are designed to eliminate the bias and drift error in the sensor data. Test results showed the Kalman Filter design is acceptable to reduce the noise in the sensor data. Moreover, the bias compensation and ZVC can reduce the drift error due to integration process as well as improve the position estimation accuracy of the quadrotor. At the time of testing, the system provided the accuracy above 90 % when it tested indoor.
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.
Inertial Navigation for Quadrotor Using Kalman Filter with Drift Compensation IJECEIAES
The main disadvantage of an Inertial Navigation System is a low accuracy due to noise, bias, and drift error in the inertial sensor. This research aims to develop the accelerometer and gyroscope sensor for quadrotor navigation system, bias compensation, and Zero Velocity Compensation (ZVC). Kalman Filter is designed to reduce the noise on the sensor while bias compensation and ZVC are designed to eliminate the bias and drift error in the sensor data. Test results showed the Kalman Filter design is acceptable to reduce the noise in the sensor data. Moreover, the bias compensation and ZVC can reduce the drift error due to integration process as well as improve the position estimation accuracy of the quadrotor. At the time of testing, the system provided the accuracy above 90 % when it tested indoor.
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.
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.
The peer-reviewed International Journal of Engineering Inventions (IJEI) is started with a mission to encourage contribution to research in Science and Technology. Encourage and motivate researchers in challenging areas of Sciences and Technology.
Aerial surveying technology is utilized in a wide range of fields throughout the world. These range from the creation of maps, to terrain analysis and research (rivers, soil erosion, coasts, etc.), urban planning, road planning (roads, rails, etc.), and vegetation research (forests, agriculture, lakes and marshland, etc.).
International Journal of Computational Engineering Research (IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
A METHOD OF TARGET TRACKING AND PREDICTION BASED ON GEOMAGNETIC SENSOR TECHNO...cscpconf
In view of the inherent defects in current airport surface surveillance system, this paper
proposes an asynchronous target-perceiving-event driven surface target surveillance scheme
based on the geomagnetic sensor technology. Furthermore, a surface target tracking and
prediction algorithm based on I-IMM is given, which is improved on the basis of IMM
algorithm in the following aspects: Weighted sum is performed on the mean of residual errors
and model probabilistic likelihood function is reconstructed, thus increasing the identification
of a true motion model; Fixed model transition probability is updated with model posterior
information, thus accelerating model switching as well as increasing the identification of a
model. In the period when a target is non-perceptible, prediction of target trajectories can be
implemented through the target motion model identified with I-IMM algorithm. Simulation
results indicate that I-IMM algorithm is more effective and advantageous in comparison with
the standard IMM algorithm.
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.
The peer-reviewed International Journal of Engineering Inventions (IJEI) is started with a mission to encourage contribution to research in Science and Technology. Encourage and motivate researchers in challenging areas of Sciences and Technology.
Aerial surveying technology is utilized in a wide range of fields throughout the world. These range from the creation of maps, to terrain analysis and research (rivers, soil erosion, coasts, etc.), urban planning, road planning (roads, rails, etc.), and vegetation research (forests, agriculture, lakes and marshland, etc.).
International Journal of Computational Engineering Research (IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
A METHOD OF TARGET TRACKING AND PREDICTION BASED ON GEOMAGNETIC SENSOR TECHNO...cscpconf
In view of the inherent defects in current airport surface surveillance system, this paper
proposes an asynchronous target-perceiving-event driven surface target surveillance scheme
based on the geomagnetic sensor technology. Furthermore, a surface target tracking and
prediction algorithm based on I-IMM is given, which is improved on the basis of IMM
algorithm in the following aspects: Weighted sum is performed on the mean of residual errors
and model probabilistic likelihood function is reconstructed, thus increasing the identification
of a true motion model; Fixed model transition probability is updated with model posterior
information, thus accelerating model switching as well as increasing the identification of a
model. In the period when a target is non-perceptible, prediction of target trajectories can be
implemented through the target motion model identified with I-IMM algorithm. Simulation
results indicate that I-IMM algorithm is more effective and advantageous in comparison with
the standard IMM algorithm.
Real Time Localization Using Receiver Signal Strength IndicatorRana Basheer
Slides from my dissertation defense. Talks about the error in localizing a transmitter by measuring the signal strength. In addition, it presents new techniques for localization using cross-correlation of fading.
Indoor localisation and dead reckoning using Sensor Tag™ BLE.Abhishek Madav
The mobile application uses readings of the Accelerometer and Gyroscope from the Sensor Tag to describe details of motion in a planar mode. The project has been implemented as a part of the EECS 221 coursework at University of California, Irvine.
Mechanization and error analysis of aiding systems in civilian and military v...ijctcm
In present scenario GPS is widely used to provide extremely accurate position information for navigation.
From, where the GPS does not give continuous localization in environments where signal blockages are
present, Inertial Navigation System comes into action. Because of sensors present in INS and time
integration process, errors get accumulated over time. Henceforth, an aiding system is integrated with INS.
The aim of this paper is to model VMS and RADAR and aid it with INS in order to overcome its errors. VMS
is aided to INS to achieve acceptable accuracy and ease of implementation, much needed in civilian
navigation. Different trajectories are generated to offer solutions in a practical scenario. Whereas, for
highly accurate positioning in military navigation a reliable aiding system, Radar has been opted. The
Kalman filter is designed and modeled as the integrating element in INS/RADAR, to provide an optimal
estimate of navigation solutions. An error analysis has been done for both INS aided VMS and INS aided
Radar systems. The navigation performance of VMS and Radar aiding system is compared and their merits
have been brought out. We besides give the readers a more honest insight of the demand for an aiding
system in different environments based on various simulation results
Linear regression models with autoregressive integrated moving average errors...IJECEIAES
The autoregressive integrated moving average (ARIMA) method has been used to model global navigation satellite systems (GNSS) measurement errors. Most ARIMA error models describe time series data of static GNSS receivers. Its application for modeling of GNSS under dynamic tests is not evident. In this paper, we aim to describe real time kinematic-GNSS (RTKGNSS) errors during dynamic tests using linear regression with ARIMA errors to establish a proof of concept via simulation that measurement errors along a trajectory logged by the RTK-GNSS can be “filtered”, which will result in improved positioning accuracy. Three sets of trajectory data of an RTK-GNSS logged in a multipath location were collected. Preliminary analysis on the data reveals the inability of the RTK-GNSS to achieve fixed integer solution most of the time, along with the presence of correlated noise in the error residuals. The best linear regression models with ARIMA errors for each data set were identified using the Akaike information criterion (AIC). The models were implemented via simulations to predict improved coordinate points. Evaluation on model residuals using autocorrelation, partial correlation, scatter plot, quantile-quantile (QQ) plot and histogram indicated that the models fitted the data well. Mean absolute errors were improved by up to 57.35% using the developed models.
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%.
27 9134 direction finding scheme for multi edit septianIAESIJEECS
In GPS denied conditions Unpretentious course structures that work especially with an excessive number of philosophies required after a colossal test that in the imaginative work gathering. The paper proposed an idea to overcome a occurring due to GPS denied condition in view of 3D inertial course system. The system would track the location for further improvement of an officer continuously in a multi-floor building of a particular area. In the midst of an urban fight situation an operation of lobbyist or putting out flames is really essential. The considerable number of fighters would be known about the area and development among themselves and to the military troop outside the building. This framework is valuable to lead the group appropriately in urban to protect the harmed individuals and it will demonstrate the change of overview.
HYBRID TOA/AOA SCHEMES FOR MOBILE LOCATION IN CELLULAR COMMUNICATION SYSTEMSijasuc
Wireless location is to determine the position of the mobile station (MS) in wireless communication
networks. Due to the measurements with large errors, location schemes give poorer performance in
non-line-of-sight (NLOS) environments. This paper illustrates methods to integrate all the available
heterogeneous measurements to achieve more accurate location estimation. The proposed hybrid
schemes combine time of arrival (TOA) at seven BSs and angle of arrival (AOA) information at the
serving BS to give location estimation of the MS. The schemes mitigate the NLOS effect simply by the
weighted sum of the intersections between seven TOA circles and the AOA line without requiring priori
knowledge of NLOS error statistics. Different NLOS models were used to evaluate the proposed methods.
It is shown by the simulation results that the proposed methods provide better location accuracy
comparing with Taylor series algorithm (TSA) and the hybrid lines of position algorithm (HLOP).
A Cooperative Localization Method based on V2I Communication and Distance Inf...IJCNCJournal
Relative positions are recent solutions to overcome the limited accuracy of GPS in urban environment. Vehicle positions obtained using V2I communication are more accurate because the known roadside unit (RSU) locations help predict errors in measurements over time. The accuracy of vehicle positions depends more on the number of RSUs; however, the high installation cost limits the use of this approach. It also depends on nonlinear localization nature. They were neglected in several research papers. In these studies, the accumulated errors increased with time due to the linearity localization problem. In the present study, a cooperative localization method based on V2I communication and distance information in vehicular networks is proposed for improving the estimates of vehicles’ initial positions. This method assumes that the virtual RSUs based on mobility measurements help reduce installation costs and facilitate in handling fault environments. The extended Kalman filter algorithm is a well-known estimator in nonlinear problem, but it requires well initial vehicle position vector and adaptive noise in measurements. Using the proposed method, vehicles’ initial positions can be estimated accurately. The experimental results confirm that the proposed method has superior accuracy than existing methods, giving a root mean square error of approximately 1 m. In addition, it is shown that virtual RSUs can assist in estimating initial positions in fault environments.
A COOPERATIVE LOCALIZATION METHOD BASED ON V2I COMMUNICATION AND DISTANCE INF...IJCNCJournal
Relative positions are recent solutions to overcome the limited accuracy of GPS in urban environment.
Vehicle positions obtained using V2I communication are more accurate because the known roadside unit
(RSU) locations help predict errors in measurements over time. The accuracy of vehicle positions depends
more on the number of RSUs; however, the high installation cost limits the use of this approach. It also
depends on nonlinear localization nature. They were neglected in several research papers. In these studies,
the accumulated errors increased with time due to the linearity localization problem. In the present study,
a cooperative localization method based on V2I communication and distance information in vehicular
networks is proposed for improving the estimates of vehicles’ initial positions. This method assumes that
the virtual RSUs based on mobility measurements help reduce installation costs and facilitate in handling
fault environments. The extended Kalman filter algorithm is a well-known estimator in nonlinear problem,
but it requires well initial vehicle position vector and adaptive noise in measurements. Using the proposed
method, vehicles’ initial positions can be estimated accurately. The experimental results confirm that the
proposed method has superior accuracy than existing methods, giving a root mean square error of
approximately 1 m. In addition, it is shown that virtual RSUs can assist in estimating initial positions in
fault environments.
HYBRID TOA/AOA SCHEMES FOR MOBILE LOCATION IN CELLULAR COMMUNICATION SYSTEMSijasuc
Wireless location is to determine the position of the mobile station (MS) in wireless communication
networks. Due to the measurements with large errors, location schemes give poorer performance in
non-line-of-sight (NLOS) environments. This paper illustrates methods to integrate all the available
heterogeneous measurements to achieve more accurate location estimation. The proposed hybrid
schemes combine time of arrival (TOA) at seven BSs and angle of arrival (AOA) information at the
serving BS to give location estimation of the MS. The schemes mitigate the NLOS effect simply by the
weighted sum of the intersections between seven TOA circles and the AOA line without requiring priori
knowledge of NLOS error statistics. Different NLOS models were used to evaluate the proposed methods.
It is shown by the simulation results that the proposed methods provide better location accuracy
comparing with Taylor series algorithm (TSA) and the hybrid lines of position algorithm (HLOP).
Localization based range map stitching in wireless sensor network under non l...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Abstract - Positioning is a fundamental component of human life to make meaningful interpretations of the environment. Without knowledge of position, human beings are like machines and have very limited capabilities to interact with the environment. Even machines in today’s world can be made smarter if positioning information is made available to them. Indoor positioning of pedestrians is the broad area considered in this thesis. A foot mounted pedestrian tracking device has been studied for this purpose. Systems which utilize foot mounted inertial navigation system has been in the literature for more than two decades. However very few real time implementations have been possible. The purpose of this thesis is to benchmark and improve the performance of one such implementation.
Design and Experimental Evaluation of Immersion and Invariance Observer for L...Jafarkeighobadi
presents a new immersion and invariance (I&I) observer for inertial microelectromechanical
systems (MEMS) sensors-based, low-cost attitude-heading
reference systems (AHRSs). Using the I&I methodology,
the observer design problem is formulated as finding a
dynamics system, called the observer, and a differentiable
manifold in the extended state space of the Euler anglesobserver dynamics. The manifold is required to be practically stable with respect to the system trajectories. By imposing this requirement, an observer is derived to robustly
estimate the Euler angles. To show the efficacy of the I&I
observer and to compare its performance with the extended
Kalman filter (EKF), rigorous simulations are performed
using the raw data of a set of urban vehicular AHRS tests.
Index Terms—Inertial navigation, Microelectromechanical systems, Nonlinear filters, Observers
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
2. stand-alone INS. One of the major benefits of knowing the
IMU’s theoretical positioning performance is that it helps to
set positioning error benchmarks under different paths and
moving patterns before diving into experiments using real
IMU. It will also help to set the overall fused localization
system performance benchmark. Besides that, no time
consuming Monte Carlo simulation needs to be performed. In
this paper, we present a theoretical analysis of INS positioning
variance in three local navigation frame directions caused by
white Gaussian noise in body frame IMU sensors. By
combining the three navigation frame results, we can obtain
the 3D position root mean squared error (RMSE). Even
though ZUPT looks appealing and the positioning
performance has been studied, it may cause some
inconvenience due to the requirement that the IMU has to be
mounted on feet. Our proposed generic performance analysis
has no limitation on the mounting points of IMU.
There are multiple noise sources in each gyroscope and
accelerometer measurement [6]. In our paper, our main focus
is to model white Gaussian noise’s impact on positioning
performance since the rest noises are either systematic or
difficult to model. Systematic error source such as constant
bias can be calibrated before the navigation period. The
prominent impact of white Gaussian noise on positioning drift
is still worth studying. The standard deviation of IMU sensor’s
white Gaussian noise is obtained using Allan Variance
technique on the Xsens MTw MEMS IMU with a tri-axial
accelerometer and tri-axial gyroscope. Gyroscope values are
constantly fed to update transformation matrix, which is
approximated using up to second order of Taylor expansion.
All accelerations variances along the three local navigation
frame axes from the beginning of navigation process are firstly
calculated. Then after combining the covariance between each
of the accelerations on the same local navigation frame axis
and perform the double integration over time, we can get the
position variance along local navigation frame, and eventually
the overall position RMSE.
Two paths have been generated to demonstrate the
accuracy of our derivation. The first path has also been used in
other publication [7]. The second path is shorter but with more
movement patterns. They will be explained in more detail in
the section III. Our proposed analysis methodology has been
tested on the path to compare with the Monte Carlo simulation
results running on the same path. Cramér-Rao Lower Bound
(CRLB) for INS perturbed by the same white Gaussian noise
has also been shown as a reference.
The rest of the paper is organized as follows. In section II,
transformation matrix consists of angular velocity values will
be analyzed, followed by the proposed method to calculate
position RMSE. In section III, we simulate two paths and
compare the derived results with Monte Carlo simulation
results and CRLB. And the acquisition of sensor’s white
Gaussian noise standard deviation is also shown in this section.
In the section IV, conclusions are drawn and future works are
also suggested.
II. METHODOLOGY
In this section, we propose a method of deriving the INS
position RMSE caused by white Gaussian noise. It focuses on
understanding the stand-alone INS positioning performance
without setting limitation on the mounting points or fusing with
other localization system.
A. Local Frame Navigation Equation
The gyroscope in a MEMS IMU measures angular velocity
whereas the accelerometer measures the specific force applied
upon IMU. Both measurements are with respect to inertial
coordinate system. Due to the fact that MEMS IMU has its
inertial sensors rigidly attached to its body, the measurements
are also made along its own body frame. The angular velocity
measurements form transformation matrix to project the
specific force measurements into the reference frame of
chosen. The reference frame acceleration is obtained after
offsetting the acceleration due to gravity. This acceleration is
integrated once to get the velocity and again to get its position
on the reference frame [8]. This paper chooses local
navigation frame as reference frame, where its x and y axes
are fixed along east and north direction and z axis is pointing
locally upward. Local navigation frame is a valid
approximation of inertial frame since the tracking period of
time is relatively short and MEMS IMU gyroscope errors are
significantly larger than the error introduced by the earth’s
rotation.
The 3D IMU measurements can be written in the form
𝜔" = (𝜔%, 𝜔', 𝜔()*
and 𝑓" = (𝑓%, 𝑓', 𝑓()*
. The subscript b
indicates the body frame. The local navigation frame
acceleration, velocity and position is given as 𝑎. =
(𝑎/, 𝑎0, 𝑎1)*
, 𝑣. = (𝑣/, 𝑣0, 𝑣1)*
and 𝑠. = (𝑠/, 𝑠0, 𝑠1)*
. The
subscript n indicates the local navigation frame and E, N, U
represent north, east and up as the three local frame axes. As
shown in [8], the transformation matrix n
bC can be updated as
( 1) ( )exp ( )
t
n n
b bk k k dt
Δ
⎛ ⎞
+ ⎜ ⎟
⎝ ⎠
= ∫ΩC C (1)
where
0 ( ) ( )
( ) ( ) 0 ( )
( ) ( ) 0
z y
z x
y x
k k
k k k
k k
ω ω
ω ω
ω ω
⎡ ⎤−
⎢ ⎥
= −⎢ ⎥
⎢ ⎥−⎣ ⎦
Ω (2)
is the skew symmetric matrix formed by 𝜔"(𝑘) . The k
represents time instance k and the ∆t is the interval in between
two adjacent IMU measurements. Small angle approximation
has been used to update 𝑪"
.
. Equation (1) can be further
expanded using rectangular rule after introducing another
skew symmetric matrix 𝑩
214
3. ( ) ( )
t
k k dt
Δ
= ∫Β Ω (3)
0 ( ) ( )
( ) ( ) 0 ( )
( ) ( ) 0
z y
z x
y x
k t k t
k k t k t
k t k t
ω ω
ω ω
ω ω
⎡ ⎤− Δ Δ
⎢ ⎥
= Δ − Δ⎢ ⎥
⎢ ⎥− Δ Δ⎣ ⎦
Β (4)
Substitute (3) into (1) and perform Taylor expansion, (1) can
be expressed as
2 3
( 1) ( ) ( ) ...
( ) ( )
2! 3!
n n
b bk k k
k k⎛ ⎞
+ + +⎜ ⎟
⎝ ⎠
= + +Β
Β Β
C C I (5)
where 𝑰 is the 3x3 identity matrix. Thus the local navigation
frame acceleration at time k can be expressed as
( )( ) ( ) ( ) 0,0, Tna k k f k g
n b b
= −C (6)
where g is the gravitational acceleration. Once the acceleration
is obtained, 𝑣. 𝑘 and 𝑠.(𝑘)can be calculated by doing single
and double integrations.
B. Position RMSE Derivation
Transformation matrix can also be expressed solely by the
known initial orientation 𝑪"
.
(1) and angular velocity values
from the beginning of time as
1
2 3( ) ( )
( 1) (1) ( ) ...
2! 3!
k
i
k kn n
k k
b b =
+ = + + + +
⎛ ⎞
⎜ ⎟
⎜ ⎟
⎝ ⎠
∏C C I
Β Β
Β (7)
where 𝑪"
.
1 is the initial orientation of IMU. In this paper, ∆t
is 0.01s since the sampling frequency is set at 100Hz. Given
this ∆t with a no large angular velocity happening at the same
time on all three axes, it is sufficient to keep only up to second
order of the Taylor expansion in (7). The truncated version of
𝑪"
.
can be written as
2 1( )
( 1) (1) ( ) ( ) ( )
21 1 2 1
jk k kin n
k i i j
b b
i i j i
−
+ = + + +∑ ∑ ∑ ∑
= = = =
⎛ ⎞
⎜ ⎟
⎜ ⎟
⎝ ⎠
C C I
Β
Β Β Β (8)
The composition for 𝑎/(𝑘) , 𝑎0(𝑘) and 𝑎1(𝑘) is similar.
Substituting (4) into (8), the 𝑎/ at time instance k+1 can be
written as three parts
( 1) ( 1) ( 1) ( 1)
_ 1 _ 2 _ 3
a k a k a k a k
E E P E P E P
+ = + + + + + (9)
where
( )( 1) ( 1) 1,1 ( 1)
_ 1
2 2
( ) ( )
1 1
12
2 ( 1)
1 1
( ) ( ) ( ) ( )
2 1 2 1
C+ = + +
−∑ ∑
= =
− −
= Δ +
− −
−∑ ∑ ∑ ∑
= = = =
⎛ ⎞
⎜ ⎟
⎜ ⎟
⎜ ⎟
⎜ ⎟
⎜ ⎟
⎜ ⎟
⎝ ⎠
n
a k k f k
E P b x
k k
i i
z y
i i
t f k
x
j jk k
i j i j
z z y y
j i j i
ω ω
ω ω ω ω
(10)
( )( 1) ( 1) 1, 2 ( 1)
_ 2
11 2
( ) ( ) ( )
21 2 1
( 1)
1
2
( ) ( )
2 1
C+ = + +
−
− Δ − Δ∑ ∑ ∑
= = =
= +
−
− Δ∑ ∑
= =
⎛ ⎞
⎜ ⎟
⎜ ⎟
⎜ ⎟
⎜ ⎟
⎜ ⎟
⎝ ⎠
n
a k k f k
E P b y
jk k
i t i j t
z y x
i j i
f k
yjk
i j t
y x
j i
ω ω ω
ω ω
(11)
( )( 1) ( 1) 1,3 ( 1)
_ 3
11 2
( ) ( ) ( )
21 2 1
( 1)
1
2
( ) ( )
2 1
C+ = + +
−
Δ − Δ∑ ∑ ∑
= = =
= +
−
− Δ∑ ∑
= =
⎛ ⎞
⎜ ⎟
⎜ ⎟
⎜ ⎟
⎜ ⎟
⎜ ⎟
⎝ ⎠
n
a k k f k
E P b z
jk k
i t i j t
y z x
i j i
f k
zjk
i j t
z x
j i
ω ω ω
ω ω
(12)
Assume the IMU is stationary before IMU starts to measure.
Due to the high sampling rate, rectangular rule has been used
again to calculate navigation frame position which is
expressed as
2
1
( ) ( 0.5) ( )
k
E E
i
s k k i a i t
=
= − + Δ∑ (13)
The variance of 𝑠/ can be acquired by substituting (9) into
(13). As we can see from (10)-(12), the three parts of 𝑎/ are
correlated due to the presence of accumulated angular velocity
values. The variance thus consists of two parts, with part one
being the each individual acceleration’s variance and part two
being the covariance between each of accelerations. It can be
written as
( ) ( )
( ) 4
2 4
1
11
3 2
( ) ( 0.5) ( )
2 ( 0.5)( 0.5) ( ), ( )
E E
E E
k
i
jk
j i
s k k i a i t
k i k j a i a j t
Var Var
Cov
=
−−
= =
= − + Δ +
− + − + Δ
∑
∑ ∑
(14)
where
( ) ( ) ( ) ( )( ), ( ) ( ) ( ) ( ) ( )E E E E E E
Cov a i a j E a i a j E a i E a j= − (15)
215
4. The covariance calculate only go up to 1k − is because of the
assumption that the initial orientation is known, as a result the
first acceleration 𝑎/(𝑘) is not correlated with the rest
accelerations. In order to calculate variance more efficiently,
we can rearrange the expression of the three parts of 𝑎/ as
( )
( )
( 1) ( ) 1,1 ( 1)
_ 1
2 2( ) ( )
( 1) 1,1
2 2 ( 1)
1 1
( ) ( ) ( ) ( )
1 1
n
a k k f k
E P b x
k k
z yn k
b
t f k
xk k
i k i k
z z y y
i i
ω ω
ω ω ω ω
+ = +
−
− −
= Δ +
− −
−∑ ∑
= =
⎛ ⎞
⎜ ⎟
⎜ ⎟
⎜ ⎟
⎜ ⎟
⎜ ⎟
⎝ ⎠
C
C (16)
( )
( )
( 1) ( ) 1,2 ( 1)
_ 2
1 2
( 1) 1,2 ( ) ( ) ( )
2
( 1)
1 2
( ) ( )
1
na k k f k
E P b y
n
k k t k k t
b z x y
f k
k y
i k t
y x
i
ω ω ω
ω ω
+ = +
− − Δ − Δ
= +
−
− Δ∑
=
⎛ ⎞
⎜ ⎟
⎜ ⎟
⎜ ⎟
⎜ ⎟
⎝ ⎠
C
C (17)
( )
( )
( 1) ( ) 1,3 ( 1)
_ 3
1 2
( 1) 1,3 ( ) ( ) ( )
2
( 1)
1 2
( ) ( )
1
na k k f k
E P b z
n
k k t k k t
b y z x
f k
k z
i k t
z x
i
ω ω ω
ω ω
+ = +
− − Δ − Δ
= +
−
− Δ∑
=
⎛ ⎞
⎜ ⎟
⎜ ⎟
⎜ ⎟
⎜ ⎟
⎝ ⎠
C
C (18)
Using the expression (16)-(18), the variance of bracket in each
part are calculated first and stored to be substitute into the next
𝑪"
.
entry. Eventually the variance of the three parts and the
covariance between them form the variance of local
navigation frame acceleration. No shortcuts were found when
calculating the covariance between accelerations on the same
axis at different time instance. Each individual part of
𝑎/(𝑖) and each individual part of 𝑎/(𝑗) are correlated.
Covariance between all the parts forms the overall
acceleration covariance. The final position RMSE at time k is
written as
( ) ( ) ( )( ) ( ) ( ) ( )E N U
RMSE k Var s k Var s k Var s k= + + (19)
III. RESULTS
In this section, two paths have been created to test the
validity of our proposed methodology. Monte Carlo simulation
and other benchmark have been used to analyze the results.
A. Allan Variance
Allan Variance is a time domain analysis technique that can
be applied to any signal to determine the character of the
underlying noise processes [6]. White Gaussian noise can be
identified on the Allan Variance plot with gradient -0.5. The
detailed description of Allan Variance can be found in [9]. The
device we are using in this paper is Mtw IMU from Xsens.
Table I given provides the summary of white Gaussian noise in
this particular Xsens IMU. Fig. 1 and Fig. 2 show the Allan
Standard Deviation plot for gyroscope noise and accelerometer
noise respectively.
TABLE I. IMU WHITE GAUSSIAN NOISE STANDARD DEVIATION
Body frame Gyroscope wσ Accelerometer aσ
X axis 0.0051 /rad s 0.015 2
/m s
Y axis 0.0051 /rad s 0.016 2
/m s
Z axis 0.0047 /rad s 0.036 2
/m s
Fig. 1. Gyroscope Allan standard deviation plot
Fig. 2. Accelerometer Allan standard deviation plot
The measurements have been taken on a time span of 3 hours.
Both graphs shows big fluctuation when the cluster time
exceeds 1000 seconds. However it is good enough for us to
identify the white Gaussian noise standard deviation for both
gyroscope and accelerometer.
B. Path Design
1) Path 1
A closed loop path was constructed to test our proposed
methodology. We assume the IMU is initially placed such that
the body frame’s x and y axes are aligned with local frame’s
216
5. east and north axes. As shown in Fig. 3, the IMU starts moving
from stationary at point [0, 0] and doing an anticlockwise loop.
The IMU is accelerating with 1 𝑚 𝑠=
along East direction for
1second followed by a 12 meter constant speed straight
movement. For the rest of the loop, the speed remains constant.
There are four 90@
turning sections with a radius of 7.5 𝑚,
during which the z axis angular velocity is set at 0.1333 𝑟𝑎𝑑 𝑠
and the centripetal acceleration sensed by IMU’s y axis is set at
0.1333 𝑚 𝑠=
in order to keep the IMU on designed path with a
constant speed. It eventually comes back to the starting point.
Fig. 3. Simulated IMU movement path 1
2) Path 2
As we will see in the next section, the position error
accumulation will reach around 10 𝑚 at 30 seconds, which will
not serve general indoor navigation purpose. A shorter path
designed as the second demonstration path. It involves more
turnings and straight line accelerations compared to path 1. The
initial alignment of IMU remains the same with that for path 1.
The IMU also starts moving from stationary at point [0, 0] and
accelerates with 1 𝑚 𝑠=
along East direction for 1second. It
maintains the same speed before deaccelerates to still at the end
of the path. During turning the centripetal acceleration and
angular velocity are adjusted accordingly to satisfy the linear
velocity. After travelling to point [8.5, 0], the IMU travels a
circle path with 1.5 𝑚 radius in clockwise direction. It
deaccelerates -1 𝑚 𝑠=
along east direction and stops at [10, 0].
Fig. 4. Simulated IMU movement path 2
In order to test our derivation, we need to know the noise
free values of IMU signal to construct the path for simulation.
The prevailing ZUPT technique requires the IMU mounted on
foot. The movement complexity makes it impossible for us to
find the noise free IMU signals for simulation. The path
generated in this paper looks simpler than ZUPT case but it
also has its own practical meaning. It can be considered similar
to the situation where people is walking while holding the
cellphone horizontally or the IMU is placed on a flat surface of
an asset which needs to be tracked.
C. Cramér-Rao Lower Bound
The CRLB provides a lower bound for mean square error.
It is used as a benchmark to assess the performance of
implemented suboptimal filtering algorithms. The Bayesian
version of CRLB is also called posterior CRLB. It is shown
that posterior CRLB for the linear Gaussian filtering problem is
equivalent to the covariance matrix of the Kalman filter [10].
Since there is no external measurement to correct the state
vector in this paper, the posterior CRLB propagation for stand-
alone IMU is Kalman filter state covariance matrix update
equation
T
(k +1) (k) (k) (k) (k)= +P F P F Q (20)
where 𝑭(𝑘) is the state transition matrix and 𝑸(𝑘) is the
process noise covariance matrix. A simplified version of
linearized error-state Kalman filter from [3] is constructed
without modelling bias into the state vector. In this particular
Kalman filter design, state vector 𝜹𝒙 is defined as 𝜹𝒙 =
(𝛿∅*
, 𝛿𝑣.
*
, 𝛿𝑠.
*
)*
, representing the attitude error, velocity error
and position error. The 𝑸(𝑘) is formed by gyroscope noise
variance, accelerometer noise variance and 𝑪"
.
. First order and
small angle approximation are used to derive the Kalman
filter. The performance of this Kalman filter does not only
depend on IMU signal noises but also depend on how we
choose the 𝑪"
.
. Different approximations of 𝑪"
.
will provide
different covariance matrices in the end.
D. Position Error Comparison
Simulation results for path 1 are shown in Fig. 5. Monte
Carlo Simulation with 5000 iterations has been performed for
comparison. In Fig. 5, the red curve Monte Carlo simulation
result shows the positioning error using Matlab built-in
function “expm” when updating 𝑪"
.
. It is the default function to
calculate exponential of matrix in Matlab. The green and blue
color curves represent CRLB curve when 𝑪"
.
using “expm”
and second order approximation respectively. The black curve
is our derived RMSE curve. All the curves stay very close up
to 50 seconds when the IMU finished the second turn. The
estimating error difference between our proposed method and
CRLB (expm) is 1.49 𝑚, 3% of distance covered up to 50s
time. After the third turn, the difference is 10.4 𝑚, 14.3% of
distance covered at that time. When completing one loop, our
proposed positioning error reaches 186.2m whereas the CRLB
(expm) is at 146.2 𝑚. The CRLB (2nd) starts to deviate further
from our derived second order RMSE after 70 seconds. For
stand-alone INS, there is no correction from other sources.
217
6. Thus 𝑪"
.
in this Kalman filter design will only be updated
using the noisy gyroscope measurements. In the long run, it
will accumulate error faster than our derived RMSE, which is
shown in Fig. 5. Our method stays close to the CRLB (expm)
and Monte Carlo simulation especially at the first half of the
path considering the error is large for stand-alone INS
navigation. It can be seen as a reasonable error growth
analysis. Another benefit of using our method is that we can
directly obtain 𝑉𝑎𝑟(𝑎.) and covariance of 𝑎.along the same
direction from our 𝑎. formulation to monitor the noise impact
on 𝑎.without doing recursive calculation like posterior CRLB.
Fig. 5. Positioning Error Comparison 1
Simulation results for path 2 are shown in Fig. 6. Monte
Carlo Simulation with 5000 iterations has been performed for
comparison. The curves start to split around 12 seconds, which
also the time the IMU just past the four relatively sharp
90J
turnings. And the split accelerates during the 360J
turning. However our derived RMSE curve still stays close to
the CRLB (expm) and Monte Carlo simulation. When path 2
is completed, our proposed derivation positioning error
reaches 6.809 m whereas the CRLB (expm) is at 5.215 m. The
difference is 6.5% of total distance travelled. It shows our
second order approximation is reasonable for both paths.
Fig. 6. Positioning Error Comparison 2
IV. CONCLUSION
In this paper, a novel method has been proposed to
calculate the stand-alone INS positioning error growth. The
proposed method breaks down transformation matrix and
provides a closed form equation analyzing the noises in
𝑎. caused by body frame acceleration and angular velocity
noises. The variance analysis of navigation frame
accelerations and positions at different time instances can help
to analyze the performance of the whole system when INS is
fused with other localization system. It also serves as an
alternative to posterior CRLB. We are currently conducting
experiment using IMU mentioned in this paper to verify our
derived results. In the future, the proposed method needs to be
applied to more routes and more complicated moving patterns.
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