Joint Estimation and Localization in Sensor Networks

This paper addresses the problem of collaborative tracking of dynamic targets in wireless sensor networks. A novel distributed linear estimator, which is a version of a distributed Kalman filter, is derived. We prove that the filter is mean square consistent in the case of static target estimation. When large sensor networks are deployed, it is common that the sensors do not have good knowledge of their locations, which affects the target estimation procedure. Unlike most existing approaches for target tracking, we investigate the performance of our filter when the sensor poses need to be estimated by an auxiliary localization procedure. The sensors are localized via a distributed Jacobi algorithm from noisy relative measurements. We prove strong convergence guarantees for the localization method and in turn for the joint localization and target estimation approach. The performance of our algorithms is demonstrated in simulation on environmental monitoring and target tracking tasks.Comment: 9 pages (two-column); 5 figures; Manuscript submitted to the 2014 IEEE Conference on Decision and Control (CDC

Joint estimation and localization in sensor networks

This paper addresses the problem of collaborative tracking of dynamic targets in wireless sensor networks. A novel distributed linear estimator, which is a version of a distributed Kalman filter, is derived. We prove that the filter is mean square consistent in the case of static target estimation. When large sensor networks are deployed, it is common that the sensors do not have good knowledge of their locations, which affects the target estimation procedure. Unlike most existing approaches for target tracking, we investigate the performance of our filter when the sensor poses need to be estimated by an auxiliary localization procedure. The sensors are localized via a distributed Jacobi algorithm from noisy relative measurements. We prove strong convergence guarantees for the localization method and in turn for the joint localization and target estimation approach. The performance of our algorithms is demonstrated in simulation on environmental monitoring and target tracking tasks

RFID Localization in Wireless Sensor Networks

Received signal strength (RSS)‐based localization of people and assets through RFID has significant benefits for logistics, security and safety. However, the accuracy of RFID localization in wireless sensor networks suffers from unrealistic antenna gain pattern assumption, and the human body has a major effect on the gain pattern of the RFID badge that the person is wearing. In this book chapter, the gain pattern due to the effect of the human body is experimentally measured and modeled. A method is presented to estimate the model parameters from multiple RSS measurements. Two joint orientation and position estimators, four‐dimensional (4D) maximum likelihood estimation (MLE) algorithm and alternating gain and position estimation (AGAPE) algorithm, are proposed to estimate the orientation and the position of the badge using RSS measurements from anchor nodes. A Bayesian lower bound on the mean squared error of the joint estimation is derived and compared with the Cramer‐Rao bound with an isotropic gain pattern. Both theoretical and experimental results show that the accuracy of position estimates can be improved with orientation estimates included in the localization system

Localization and time synchronization in wireless sensor networks: a unified approach

Localization and synchronization are two important issues in communication systems and wireless sensor networks. These two problems are addressed in many applications, and share many aspects in common. However, these two problems are traditionally treated separately. In this paper, we present a unified framework to jointly solve these two problems at the same time. The maximum likelihood joint estimation of location and timing is firstly derived. Then, a more computationally efficient two-stage least square method is proposed. The Cramer-Rao lower bound for the joint localization and time synchronization is also derived. Simulation results show that the performances of the maximum likelihood and two-stage least square estimators can both achieve the Cramer-Rao lower bound. ©2008 IEEE.published_or_final_versionThe IEEE Asia-Pacific Conference on Circuits and Systems (APCCAS 2008), Macao, China, 30 November-3 December 2008. In Proceedings of IEEE APCCAS, 2008, p. 594-59

Distributed Maximum Likelihood for Simultaneous Self-localization and Tracking in Sensor Networks

We show that the sensor self-localization problem can be cast as a static parameter estimation problem for Hidden Markov Models and we implement fully decentralized versions of the Recursive Maximum Likelihood and on-line Expectation-Maximization algorithms to localize the sensor network simultaneously with target tracking. For linear Gaussian models, our algorithms can be implemented exactly using a distributed version of the Kalman filter and a novel message passing algorithm. The latter allows each node to compute the local derivatives of the likelihood or the sufficient statistics needed for Expectation-Maximization. In the non-linear case, a solution based on local linearization in the spirit of the Extended Kalman Filter is proposed. In numerical examples we demonstrate that the developed algorithms are able to learn the localization parameters.Comment: shorter version is about to appear in IEEE Transactions of Signal Processing; 22 pages, 15 figure

Space-Time Hierarchical-Graph Based Cooperative Localization in Wireless Sensor Networks

It has been shown that cooperative localization is capable of improving both the positioning accuracy and coverage in scenarios where the global positioning system (GPS) has a poor performance. However, due to its potentially excessive computational complexity, at the time of writing the application of cooperative localization remains limited in practice. In this paper, we address the efficient cooperative positioning problem in wireless sensor networks. A space-time hierarchical-graph based scheme exhibiting fast convergence is proposed for localizing the agent nodes. In contrast to conventional methods, agent nodes are divided into different layers with the aid of the space-time hierarchical-model and their positions are estimated gradually. In particular, an information propagation rule is conceived upon considering the quality of positional information. According to the rule, the information always propagates from the upper layers to a certain lower layer and the message passing process is further optimized at each layer. Hence, the potential error propagation can be mitigated. Additionally, both position estimation and position broadcasting are carried out by the sensor nodes. Furthermore, a sensor activation mechanism is conceived, which is capable of significantly reducing both the energy consumption and the network traffic overhead incurred by the localization process. The analytical and numerical results provided demonstrate the superiority of our space-time hierarchical-graph based cooperative localization scheme over the benchmarking schemes considered.Comment: 14 pages, 15 figures, 4 tables, accepted to appear on IEEE Transactions on Signal Processing, Sept. 201

Graphical model-based approaches to target tracking in sensor networks: an overview of some recent work and challenges

Sensor Networks have provided a technology base for distributed target tracking applications among others. Conventional centralized approaches to the problem lack scalability in such a scenario where a large number of sensors provide measurements simultaneously under a possibly non-collaborating environment. Therefore research efforts have focused on scalable, robust, and distributed algorithms for the inference tasks related to target tracking, i.e. localization, data association, and track maintenance. Graphical models provide a rigorous tool for development of such algorithms by modeling the information structure of a given task and providing distributed solutions through message passing algorithms. However, the limited communication capabilities and energy resources of sensor networks pose the additional difculty of considering the tradeoff between the communication cost and the accuracy of the result. Also the network structure and the information structure are different aspects of the problem and a mapping between the physical entities and the information structure is needed. In this paper we discuss available formalisms based on graphical models for target tracking in sensor networks with a focus on the aforementioned issues. We point out additional constraints that must be asserted in order to achieve further insight and more effective solutions