Dynamic Information-theoretic Sensor Selection Schemes for Target Tracking Applications - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Dynamic Information-theoretic Sensor Selection Schemes for Target Tracking Applications write by Farzaneh Razavi Armaghani. This book was released on 2013. Dynamic Information-theoretic Sensor Selection Schemes for Target Tracking Applications available in PDF, EPUB and Kindle. Wireless Sensor Networks (WSNs) provide ad hoc infrastructure for the applications that must operate in remote and harsh environments, e.g. target tracking, wildlife tracking, and environmental monitoring. The primary factors driving such pervasive applications are the flexibility, fault tolerance, high sensing fidelity, low cost, and rapid deployment characteristics of WSNs. Energy efficiency is a critical feature of WSNs because sensor nodes run on batteries. Batteries are generally difficult to recharge after deployment. The principal way of increasing the network lifetime is to minimise the number of active sensors in the region of interest.Target tracking applications, being some of the main applications in WSNs, need continuous location estimations of moving objects. The requirement for high accuracy of estimations poses an additional challenge to WSNs. Accuracy of estimations can be improved by activating more sensors. However, this approach to increasing accuracy can result in higher energy consumption and a shorter network lifetime. Therefore, a reliable and effective sensor selection scheme is necessary to rotate the tracking task between the optimal sets of active sensors, to balance the trade-off between estimation accuracy and network lifetime.This thesis addresses the problem of the accuracy-lifetime trade-off in sensor selection, for three types of target tracking applications: single target tracking, multiple-target tracking, and group target tracking. Particle filtering is a widely used Bayesian estimation method that is capable of solving realistic problems. We propose, develop, implement, and validate predictive sensor selection schemes to find the best set of active sensors heuristically. The proposed schemes take advantage of particle filtering to calculate the sensor information utilities based on the predicted locations of targets. The use of sensor information utilities and sensor energy parameters in the design of selection cost metrics facilitates the formation of best sets of active sensors. In addition, the problem of the accuracy-lifetime trade-off deals with the type of data processing mechanism. The proposed schemes investigate the impact of both local and central data processing on the trade-off. Group target tracking applications demand a compatible clustering algorithm to accurately estimate the groups and their constituent targets. The proposed clustering framework adaptively finds the target groups based on the notation of trajectory mining and graph theory, and is incorporated in the design of a region-based sensor selection scheme for group target tracking. To deal with the accuracy-lifetime trade-off, our sensor selection philosophy is to select a dynamic number of sensors at anytime.This thesis presents implementation and evaluation details of the proposed schemes. Extensive simulations and evaluations have been performed to show the energy efficiency of the proposed schemes in accurate tracking of the single, multiple, or grouped targets. The proposed schemes can be applied in different tracking and monitoring applications, e.g. wildlife tracking, environmental monitoring, bushfire tracking, and traffic management. The application is made feasible by redefining the concept of information utility based on the physical property of interest, e.g. sound and temperature. We believe that the adoption of the proposed schemes would assist any tracking application to dynamically reconfigure the sensor activities, so that network lifetime is prolonged and a high quality of information is attained.
Integrated Tracking, Classification, and Sensor Management
Integrated Tracking, Classification, and Sensor Management - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Integrated Tracking, Classification, and Sensor Management write by Mahendra Mallick. This book was released on 2012-12-03. Integrated Tracking, Classification, and Sensor Management available in PDF, EPUB and Kindle. A unique guide to the state of the art of tracking, classification, and sensor management This book addresses the tremendous progress made over the last few decades in algorithm development and mathematical analysis for filtering, multi-target multi-sensor tracking, sensor management and control, and target classification. It provides for the first time an integrated treatment of these advanced topics, complete with careful mathematical formulation, clear description of the theory, and real-world applications. Written by experts in the field, Integrated Tracking, Classification, and Sensor Management provides readers with easy access to key Bayesian modeling and filtering methods, multi-target tracking approaches, target classification procedures, and large scale sensor management problem-solving techniques. Features include: An accessible coverage of random finite set based multi-target filtering algorithms such as the Probability Hypothesis Density filters and multi-Bernoulli filters with focus on problem solving A succinct overview of the track-oriented MHT that comprehensively collates all significant developments in filtering and tracking A state-of-the-art algorithm for hybrid Bayesian network (BN) inference that is efficient and scalable for complex classification models New structural results in stochastic sensor scheduling and algorithms for dynamic sensor scheduling and management Coverage of the posterior Cramer-Rao lower bound (PCRLB) for target tracking and sensor management Insight into cutting-edge military and civilian applications, including intelligence, surveillance, and reconnaissance (ISR) With its emphasis on the latest research results, Integrated Tracking, Classification, and Sensor Management is an invaluable guide for researchers and practitioners in statistical signal processing, radar systems, operations research, and control theory.
Sensor Management for Target Tracking Applications
Sensor Management for Target Tracking Applications - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Sensor Management for Target Tracking Applications write by Per Boström-Rost. This book was released on 2021-04-12. Sensor Management for Target Tracking Applications available in PDF, EPUB and Kindle. Many practical applications, such as search and rescue operations and environmental monitoring, involve the use of mobile sensor platforms. The workload of the sensor operators is becoming overwhelming, as both the number of sensors and their complexity are increasing. This thesis addresses the problem of automating sensor systems to support the operators. This is often referred to as sensor management. By planning trajectories for the sensor platforms and exploiting sensor characteristics, the accuracy of the resulting state estimates can be improved. The considered sensor management problems are formulated in the framework of stochastic optimal control, where prior knowledge, sensor models, and environment models can be incorporated. The core challenge lies in making decisions based on the predicted utility of future measurements. In the special case of linear Gaussian measurement and motion models, the estimation performance is independent of the actual measurements. This reduces the problem of computing sensing trajectories to a deterministic optimal control problem, for which standard numerical optimization techniques can be applied. A theorem is formulated that makes it possible to reformulate a class of nonconvex optimization problems with matrix-valued variables as convex optimization problems. This theorem is then used to prove that globally optimal sensing trajectories can be computed using off-the-shelf optimization tools. As in many other fields, nonlinearities make sensor management problems more complicated. Two approaches are derived to handle the randomness inherent in the nonlinear problem of tracking a maneuvering target using a mobile range-bearing sensor with limited field of view. The first approach uses deterministic sampling to predict several candidates of future target trajectories that are taken into account when planning the sensing trajectory. This significantly increases the tracking performance compared to a conventional approach that neglects the uncertainty in the future target trajectory. The second approach is a method to find the optimal range between the sensor and the target. Given the size of the sensor's field of view and an assumption of the maximum acceleration of the target, the optimal range is determined as the one that minimizes the tracking error while satisfying a user-defined constraint on the probability of losing track of the target. While optimization for tracking of a single target may be difficult, planning for jointly maintaining track of discovered targets and searching for yet undetected targets is even more challenging. Conventional approaches are typically based on a traditional tracking method with separate handling of undetected targets. Here, it is shown that the Poisson multi-Bernoulli mixture (PMBM) filter provides a theoretical foundation for a unified search and track method, as it not only provides state estimates of discovered targets, but also maintains an explicit representation of where undetected targets may be located. Furthermore, in an effort to decrease the computational complexity, a version of the PMBM filter which uses a grid-based intensity to represent undetected targets is derived.
Intelligent Wireless Binary Sensor Network System for Indoor Multiple Target Tracking
Intelligent Wireless Binary Sensor Network System for Indoor Multiple Target Tracking - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Intelligent Wireless Binary Sensor Network System for Indoor Multiple Target Tracking write by Jiang Lu. This book was released on 2015. Intelligent Wireless Binary Sensor Network System for Indoor Multiple Target Tracking available in PDF, EPUB and Kindle. Multiple human tracking is desirable for many applications ranging from surveillance to gait biometrics, robotics and intelligent space. In recent, the low cost, low power consumption sensors, such as acoustic sensor, thermal sensors, pressure sensors, photonic sensors provide a new way to achieve multiple human tracking compared with the conventional tracking, such as Radar, Sonar and video tracking. Advances in sensor network technologies enable the development of distributed tracking systems. In this thesis, we attempt to explain the multiple human tracking problems with our binary pyroelectric infrared (PIR) sensors. We will cover from the designs of hardware sensor nodes to the problems of sensor node selection and calibration to the framework of binary compressive tracking to the implementation of distributed wireless senor system for tracking. In particular, we will investigate (a) A compressive multiple human tracking system using space encoding / decoding methods. (b) A Binary compressive tracking framework using low density parity check (LDPC) matrix and linear programming for encoding and decoding schemes. (c) A distributed information filter algorithm for tracking. (d) The information gain based sensor selection scheme and NMF based calibration scheme. The major accomplishments of this thesis include the following four aspects: (1) Space encoding and measurement decoding schemes. The space encoding scheme is based on the low density parity check (LDPC) matrix, which converts k-sparse target position vectors into different codewords. The measurement decoding scheme contains linear programming based localization and graphical model based tracking algorithms, which converts codewords into the states of multiple targets. A posterior Cram\'er-Rao bound analysis is utilized to achieve the tradeoff between the compression ratio of measurements and the accuracy of the tracking system. (2) Information driven sensors selection scheme. The information gain is used to dynamically adding sensors to maximize the information gain. The sensor selection procedure provides the maximum information gain for the whole sensor system which contains the minimum sensor numbers that can be used for tracking. (3) NMF based sensor calibration scheme. We provide a probability model for sensor calibration. Nonnegtive Matrix Factorization method is used to update the probability model. The calibration of sensor parameters, positions and orientations, then can be computed from the updated probability model. (4) Graphical model based problem illustration. We describe the graphical model for tracking algorithms. Various hidden variables can be added in graphical model. Also, a factor graph of tracking is provided to describe the distribute way of belief propagation.
Handbook of Dynamic Data Driven Applications Systems
Handbook of Dynamic Data Driven Applications Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Handbook of Dynamic Data Driven Applications Systems write by Frederica Darema. This book was released on 2023-10-16. Handbook of Dynamic Data Driven Applications Systems available in PDF, EPUB and Kindle. This Second Volume in the series Handbook of Dynamic Data Driven Applications Systems (DDDAS) expands the scope of the methods and the application areas presented in the first Volume and aims to provide additional and extended content of the increasing set of science and engineering advances for new capabilities enabled through DDDAS. The methods and examples of breakthroughs presented in the book series capture the DDDAS paradigm and its scientific and technological impact and benefits. The DDDAS paradigm and the ensuing DDDAS-based frameworks for systems’ analysis and design have been shown to engender new and advanced capabilities for understanding, analysis, and management of engineered, natural, and societal systems (“applications systems”), and for the commensurate wide set of scientific and engineering fields and applications, as well as foundational areas. The DDDAS book series aims to be a reference source of many of the important research and development efforts conducted under the rubric of DDDAS, and to also inspire the broader communities of researchers and developers about the potential in their respective areas of interest, of the application and the exploitation of the DDDAS paradigm and the ensuing frameworks, through the examples and case studies presented, either within their own field or other fields of study. As in the first volume, the chapters in this book reflect research work conducted over the years starting in the 1990’s to the present. Here, the theory and application content are considered for: Foundational Methods Materials Systems Structural Systems Energy Systems Environmental Systems: Domain Assessment & Adverse Conditions/Wildfires Surveillance Systems Space Awareness Systems Healthcare Systems Decision Support Systems Cyber Security Systems Design of Computer Systems The readers of this book series will benefit from DDDAS theory advances such as object estimation, information fusion, and sensor management. The increased interest in Artificial Intelligence (AI), Machine Learning and Neural Networks (NN) provides opportunities for DDDAS-based methods to show the key role DDDAS plays in enabling AI capabilities; address challenges that ML-alone does not, and also show how ML in combination with DDDAS-based methods can deliver the advanced capabilities sought; likewise, infusion of DDDAS-like approaches in NN-methods strengthens such methods. Moreover, the “DDDAS-based Digital Twin” or “Dynamic Digital Twin”, goes beyond the traditional DT notion where the model and the physical system are viewed side-by-side in a static way, to a paradigm where the model dynamically interacts with the physical system through its instrumentation, (per the DDDAS feed-back control loop between model and instrumentation).
