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distributed target tracking in Sensor Networks by Consistency Algorithm and Semantic Moving Computing of Internet of Things

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IEEE ACCESS 2022年 10卷 51634-51642页

作者： Wang, Yun Liu, Han Zhou, Jun Wan, Yi Shanxi Polytech Coll Dept Comp Engn Taiyuan 030006 Peoples R China Hebei Univ Coll Qual & Tech Supervis Baoding 071002 Peoples R China Chongqing Business Vocat Coll Sch Artificial Intelligence Chongqing 401331 Peoples R China Southwest Technol & Engn Res Inst Chongqing 401331 Peoples R China Southwest Univ Sci & Technol Sch Informat Engn Mianyang 621010 Sichuan Peoples R China

The target tracking algorithm of mobile wireless sensor networks involves target motion trend prediction and subsequent node guidance. This study aims to solve the problems of global consistency of node information and significant errors in forecasting fast-moving targets' trajectories through traditional distributed tracking methods in sensor networks. Initially, the average consistency algorithm is used to average the local measurements of each node to achieve global consistency. Then, semantic moving computing of the Internet of Things calculates and analyzes the node movement to support the subsequent movement guidance of nodes and target movement prediction. Finally, the simulation experiment is carried out to evaluate the commonly used target trajectory prediction model. The simulation results show that the node movement algorithm by average consistency can effectively improve the positioning accuracy of the network for moving targets. Besides, the positioning error decreases with the increase of the sensing radius R, the number of moving nodes nm, and the total number of nodes ns deployed in a particular range in a two-dimensional (2D) space. The positioning error after node movement in 2D space is about 20%-30%R lower than that in a static state. After node movement in a three-dimensional (3D) space, the positioning error is about 40%-50%R lower than in a dormant state. When the target moves at a speed greater than 7m/s, the consistency-based moving computing algorithm's target loss rate and tracking errors are about 0 similar to 10% and 1.5%similar to 2% lower than the target tracking algorithm via Kalman Filter. Therefore, the algorithm reported here can precisely track the high-speed moving target. The existing research on point target tracking has problems of insufficient accuracy and robustness. The algorithm proposed here has stronger robustness, reduced data error in multi-node, and more flexible node movements, providing a reference for the subsequen

关键词： Consistency algorithm moving computing algorithm mobile wireless sensor network distributed target tracking

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distributed target tracking in sensor networks using multi-step consensus

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IET RADAR SONAR AND NAVIGATION 2018年第9期12卷 998-1004页

作者： Al Ali, Khaled Obaid Ilic, Nemanja Stankovic, Milos S. Stankovic, Srdjan S. Etimad Abu Dhabi U Arab Emirates Vlatacom Inst Belgrade Serbia Coll Tech & Technol Krusevac Serbia Univ Belgrade Fac Elect Engn Innovat Ctr Belgrade Serbia Univ Belgrade Fac Elect Engn Belgrade Serbia

The authors propose a new algorithm for distributed target tracking by sensor networks with sensors having limited sensing range (radars, sonars, cameras), using an adaptive multi-step consensus scheme. The proposed distributed adaptation strategy represents the core element of the algorithm, providing: (i) asymptotic consensus gains giving the desired importance to the nodes observing the target, and (ii) fast convergence rate of the consensus scheme, enabling efficient implementation. Another contribution of this study is a theoretical study of the algorithm properties, including asymptotic stability and reduction of noise influence. The given simulation results show that the proposed algorithm outperforms the existing algorithms, preserving, at the same time, much lower communication and computation requirements.

关键词： target tracking wireless sensor networks convergence distributed target tracking sensor networks adaptive multistep consensus scheme limited sensing range distributed adaptation strategy asymptotic consensus gains fast convergence rate asymptotic stability noise influence reduction

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Consistent Kalman Filter for distributed target tracking with Polar Measurements 45

Consistent Kalman Filter for Distributed Target Tracking wit...

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45th Annual Conference of the IEEE Industrial Electronics Society (IECON)

作者： Lee, Yunha Ra, Won-Sang Suh, Ui-Suk Whang, Ick-Ho Handong Global Univ Pohang South Korea Agcy Def Dev Daejeon South Korea

ISBN: (纸本)9781728148786

This paper deals with a distributed target tracking problem for multiple autonomous vehicles sharing the polar measurements obtained from heterogeneous sensors. To enhance the unsatisfactory convergence property of the existing filtering techniques, a linear consistent Kalman filter (CKF) is proposed. The key idea of our approach is to model the relation between the target state and the polar measurements as a linear uncertain measurement equation. This model has a distinctive form because the uncertain coordinate transform matrix is multiplied, and correlated, to the available measurement matrix. The design objective of the proposed CKF is set to achieve the optimal performance in the sense of estimation error mean even when the coordinate transform uncertainty exists. Different from the previous methods, it provides the consistent target state estimates and makes the heterogeneous sensor fusion easy. Through the simulations, the effectiveness of the proposed method is verified.

关键词： distributed target tracking polar measurements multiple autonomous vehicles

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Consensus-based distributed adaptive target tracking in camera networks using Integrated Probabilistic Data Association

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2018年第1期2018卷 1-16页

作者： Al Ali, Khaled Obaid Ilic, Nemanja Stankovic, Milos S. Stankovic, Srdjan S. Etimad Abu Dhabi U Arab Emirates Vlatacom Inst Bulevar Milutina Milankov 5 Belgrade 11000 Serbia Coll Tech & Technol Krusevac 37000 Serbia Univ Belgrade Innovat Ctr Fac Elect Engn Belgrade 11000 Serbia Univ Belgrade Fac Elect Engn Belgrade 11000 Serbia

In this paper, a novel consensus-based adaptive algorithm for distributed target tracking in large scale camera networks is presented, aimed at situations characterized by limited sensing range, high-level clutter, and possibly occulted targets. The concept of Integrated Probabilistic Data Association (IPDA) is introduced in the distributed adaptive tracker design so that the proposed algorithm, named IPDA Adaptive Consensus Filter (IPDA-ACF), incorporates probabilities of acquiring target-originated measurements, conditioned on either target perceivability or target existence. A distributed adaptation scheme represents the core element of the algorithm, allowing fast convergence under a large variety of operating conditions, emphasizing the influence of the nodes with the highest probability of obtaining target-originated measurements. A theoretical analysis of stability and reduction of noise influence allows getting an insight into the relationship between the local trackers and the global consensus scheme. A comparison with analogous existing methods done by extensive simulations shows that the proposed method achieves the best performance, in spite of lower communication and computation requirements.

关键词： Camera networks distributed target tracking Integrated Probabilistic Data Association Consensus Decentralized adaptation

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distributed target tracking by time of arrival and received signal strength with unknown path loss exponent

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IET SIGNAL PROCESSING 2015年第9期9卷 681-686页

作者： Li, Wenling Jia, Yingmin Beihang Univ BUAA Res Div 7 Beijing 100191 Peoples R China

The problem of distributed target tracking using time of arrival and received signal strength with unknown path loss exponent (PLE) is studied. The PLE is modelled as a Markov chain with three states and an adaptive gridding strategy is adopted to adjust the PLE recursively in a bounded interval. Therefore, the target tracking model is formulated as a jump Markov non-linear system. The interacting multiple model () estimator is applied to derive the target state estimates for each sensor and the covariance intersection approach is used to fuse sensor-based estimates in a distributed fashion. Simulation results show a promising performance for the proposed filter.

关键词： time-of-arrival estimation target tracking RSSI tracking filters Markov processes state estimation sensor fusion covariance matrices distributed target tracking time-of-arrival estimation received signal strength path loss exponent PLE Markov chain adaptive griding strategy jump Markov nonlinear system interacting multiple model estimator target state estimation covariance intersection approach sensor fusion based estimation distributed fashion

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Energy-Efficient distributed target tracking in Wireless Video Sensor Networks

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INTERNATIONAL JOURNAL OF WIRELESS INFORMATION NETWORKS 2015年第2期22卷 105-115页

作者： Fang, Wu Wang, Dian Hong Wang, Yong Suzhou Inst Trade & Commerce Mech & Informat Dept Suzhou Peoples R China China Univ Geosci Fac Mech & Elect Informat Wuhan 430074 Peoples R China

In order to prolong the lifetime of wireless video sensor networks (WVSNs), many methods for visual target tracking are proposed to reduce the energy cost of battery-powered video sensor nodes. Focus on reducing the amounts of image data for computation and communication, this paper presents an energy-efficient method for distributed target tracking in WVSNs. The major contributions of the proposed approach are as follows. First, each activated video node uses real-time background subtraction based on balanced compressive sensing adaptive gaussian mixture Model for target detection. Second, targets in different views are associated by homograph projection of the position and hue histogram. Third, a Sign of Innovation based consensus filter is proposed for distributed target tracking. Experimental results show the method can effectively reduce the energy cost and achieve satisfying performance in real scenes.

关键词： distributed target tracking Wireless video sensor networks Compressive sensing target association Consensus filter

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distributed Variational Measurement Update for Extended target tracking With Random Matrix

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第4期60卷 3792-3806页

作者： Jiao, Qinqin Yang, Xiaojun Changan Univ Sch Informat Engn Xian 710064 Peoples R China

The Gamma Gaussian inverse Wishart (GGIW) extended target model leads to an elegant implementation of the Bayesian estimation for the extended target tracking. In this article, we proposed the distributed variational Bayesian measurement update for the GGIW implementation. First, a centralized multisensor measurement update algorithm for GGIW model was derived using variational Bayesian inference. Then, we proposed the distributed variational measurement update based on the alternating direction method of multipliers (ADMM) algorithm. Furthermore, we introduced a regularization term representing a constraint of the prior information and the optimality of the estimation. We presented a heuristic solver which divided the formulated optimization problem into three subproblems and solved alternately in the framework of ADMM. The simulation results verify the effectiveness and show the comparable performance to that of the centralized algorithm with faster convergence.

关键词： Bayes methods target tracking Noise measurement Symmetric matrices Estimation Kinematics Convex functions distributed target tracking extended target tracking random matrix (RM) variational Bayes (VB)

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Belief consensus algorithms for fast distributed target tracking in wireless sensor networks

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SIGNAL PROCESSING 2014年 95卷 149-160页

作者： Savic, Vladimir Wymeersch, Henk Zazo, Santiago Univ Politecn Madrid Signal Proc Applicat Grp E-28040 Madrid Spain Linkoping Univ Dept Elect Engn ISY S-58183 Linkoping Sweden Chalmers Univ Technol Dept Signals & Syst S-41296 Gothenburg Sweden

In distributed target tracking for wireless sensor networks, agreement on the target state can be achieved by the construction and maintenance of a communication path, in order to exchange information regarding local likelihood functions. Such an approach lacks robustness to failures and is not easily applicable to ad-hoc networks. To address this, several methods have been proposed that allow agreement on the global likelihood through fully distributed belief consensus (BC) algorithms, operating on local likelihoods in distributed particle filtering (DPF). However, a unified comparison of the convergence speed and communication cost has not been performed. In this paper, we provide such a comparison and propose a novel BC algorithm based on belief propagation (BP). According to our study, DPF based on metropolis belief consensus (MBC) is the fastest in loopy graphs, while DPF based on BP consensus is the fastest in tree graphs. Moreover, we found that BC-based DPF methods have lower communication overhead than data flooding when the network is sufficiently sparse. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Belief consensus Belief propagation distributed target tracking Particle filtering Wireless sensor networks

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Adaptive Consensus-Based distributed target tracking in Sensor Networks With Limited Sensing Range

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IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 2014年第2期22卷 778-785页

作者： Ilic, Nemanja Stankovic, Milos S. Stankovic, Srdjan S. Univ Belgrade Sch Elect Engn Belgrade 11000 Serbia Royal Inst Technol Sch Elect Engn S-10044 Stockholm Sweden

In this brief, consensus-based algorithms for distributed target tracking are discussed, and a new algorithm with decentralized adaptation is proposed for sensor networks with limited sensing range. The given comparative analysis shows that tuning of the gains in the consensus scheme is of crucial importance for getting simple, yet efficient tracking algorithms, requiring the exchange of only local state estimates between the nodes. The proposed algorithm, which gives higher importance to the nodes currently receiving measurements, is based on the exchange of additional binary information and represents a robust and efficient tool for practice. Stability of the algorithm is theoretically discussed on a given scenario. Selected examples illustrate its performance in terms of the estimation error and the disagreement between the nodes.

关键词： Consensus decentralized adaptation distributed target tracking sensor networks stability

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Multi-target tracking in distributed sensor networks using particle PHD filters

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SIGNAL PROCESSING 2019年 159卷 130-146页

作者： Leonard, Mark R. Zoubir, Abdelhak M. Tech Univ Darmstadt Inst Telecommun Signal Proc Grp D-64283 Darmstadt Germany

Multi-target tracking is an important problem in civilian and military applications. This paper investigates multi-target tracking in distributed sensor networks. Data association, which arises particularly in multi-object scenarios, can be tackled by various solutions. We consider sequential Monte Carlo implementations of the Probability Hypothesis Density (PHD) filter based on random finite sets. This approach circumvents the data association issue by jointly estimating all targets in the region of interest. To this end, we develop the Diffusion Particle PHD Filter (D-PPHDF) as well as a centralized version, called the Multi-Sensor Particle PHD Filter (MS-PPHDF). Their performance is evaluated in terms of the Optimal Sub pattern Assignment (OSPA) metric, benchmarked against a distributed extension of the Posterior Cramer-Rao Lower Bound (PCRLB), and compared to the performance of an existing distributed PHD Particle Filter. Furthermore, the robustness of the proposed tracking algorithms against outliers and their performance with respect to different amounts of clutter is investigated. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Multi-target tracking distributed target tracking Particle filter PHD Filter Robustness Posterior Cramer-Rao lower bound

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