Survey of location-centric target tracking with mobile elements in wireless sensor networks

介绍目标跟踪的过程以及移动跟踪的特点;通过区分目标定位为主的方法和目标探测为主的方法,介绍定位为主的移动式目标跟踪方法(称为目标的移动式定位跟踪; )的研究现状;分析和比较不同方法的特点和应用领域,发现现有方法虽然可以提高跟踪质量、降低网络整体能耗,但是还存在一些问题。基于此,总结目标的移动; 式定位跟踪方法在方法类型、网络结构和节点模型等方面可能存在的研究热点,指出其研究和发展趋势。The basic process of target tracking and the properties of tracking; solutions with mobile elements were introduced. By distinguishing; location-centric methods and detection-centric methods, the current; research status of the location-centric target tracking methods were; reviewed. The properties and application fields of different solutions; were analyzed and compared. Although the existing solutions can; significantly improve tracking quality and reduce energy consumption of; the whole network, there are also some problems. Based on these; discoveries, some possible research hotspots of mobile solutions were; summarized in many aspects, such as method types, network architecture,; node model, and so on, indicating the future direction of research and; development.国家自然科学基金资助项目; 国家科技支撑计划项

Cooperative localization with information-seeking control

We propose a Bayesian method for cooperative localization and control in mobile agent networks. Distributed, cooperative self-localization of each agent is supported by an information-seeking control of the movement of the agents. For cooperative localization, the SPAWN message passing scheme is used. Cooperative control is achieved by maximizing the negative joint posterior entropy of the agent states via a gradient ascent. The localization part of our method provides the control part with sample-based probabilistic information. Simulation results demonstrate intelligent behavior of the agents and excellent localization accuracy

Cooperative Simultaneous Localization and Synchronization in Mobile Agent Networks

Cooperative localization in agent networks based on interagent time-of-flight measurements is closely related to synchronization. To leverage this relation, we propose a Bayesian factor graph framework for cooperative simultaneous localization and synchronization (CoSLAS). This framework is suited to mobile agents and time-varying local clock parameters. Building on the CoSLAS factor graph, we develop a distributed (decentralized) belief propagation algorithm for CoSLAS in the practically important case of an affine clock model and asymmetric time stamping. Our algorithm allows for real-time operation and is suitable for a time-varying network connectivity. To achieve high accuracy at reduced complexity and communication cost, the algorithm combines particle implementations with parametric message representations and takes advantage of a conditional independence property. Simulation results demonstrate the good performance of the proposed algorithm in a challenging scenario with time-varying network connectivity.Comment: 13 pages, 6 figures, 3 tables; manuscript submitted to IEEE Transaction on Signal Processin

Distributed Estimation with Information-Seeking Control in Agent Network

We introduce a distributed, cooperative framework and method for Bayesian estimation and control in decentralized agent networks. Our framework combines joint estimation of time-varying global and local states with information-seeking control optimizing the behavior of the agents. It is suited to nonlinear and non-Gaussian problems and, in particular, to location-aware networks. For cooperative estimation, a combination of belief propagation message passing and consensus is used. For cooperative control, the negative posterior joint entropy of all states is maximized via a gradient ascent. The estimation layer provides the control layer with probabilistic information in the form of sample representations of probability distributions. Simulation results demonstrate intelligent behavior of the agents and excellent estimation performance for a simultaneous self-localization and target tracking problem. In a cooperative localization scenario with only one anchor, mobile agents can localize themselves after a short time with an accuracy that is higher than the accuracy of the performed distance measurements.Comment: 17 pages, 10 figure

Modified Iterated Extended Kalman Filter for Mobile Cooperative Tracking System

Tracking a mobile node using wireless sensor network (WSN) under cooperative system among anchor node and mobile node, has been discussed in this work, interested to the indoor positioning applications. Developing an indoor location tracking system based on received signal strength indicator (RSSI) of WSN is considered cost effective and the simplest method. The suitable technique for estimating position out of RSSI measurements is the extended Kalman filter (EKF) which is especially used for non linear data as RSSI. In order to reduce the estimated errors from EKF algorithm, this work adopted forward data processing of the EKF algorithm to improve the accuracy of the filtering output, its called iterated extended Kalman filter (IEKF). However, using IEKF algorithm should know the stopping criterion value that is influenced to the maximum number iterations of this system. The number of iterations performed will be affected to the computation time although it can improve the estimation position. In this paper, we propose modified IEKF for mobile cooperative tracking system within only 4 iterations number. The ilustrated results using RSSI measurements and simulation in MATLAB show that our propose method have capability to reduce error estimation percentage up to 19.3% , with MSE (mean square error) 0.88 m compared with conventional IEKF algorithm with MSE 1.09 m. The time computation perfomance of our propose method achived in 3.55 seconds which is better than adding more iteration process.     

Fast and accurate cooperative tracking in wireless networks

The utility of wireless networks for many applications is increased if the locations of the nodes in the network can be tracked based on the measurements between communicating nodes. Many applications, such as tracking fire fighters in large buildings, require the deployment of mobile ad hoc networks. Real-time tracking in such environments is a challenging task, particularly combined with restrictions on computational and communication resources in mobile devices. In this paper we present a new algorithm using the Bayesian framework for cooperative tracking of nodes, which allows accurate tracking over large areas using only a small number of anchor nodes. The proposed algorithm requires lower computational and communication resources than existing algorithms. Simulation results show that the algorithm performs well with the tracking error being close to the posterior Cramer-Rao lower bound that we derive for cooperative tracking. Experimental results for a network deployed in an indoor office environment with external GPS referenced anchor nodes are presented. A computationally simple indoor range error model for measurements at the 5.8 GHz ISM band that yields positioning accuracy close to that obtained when using the actual range error distribution is also presented.Thuraiappah Sathyan, and Mark Hedle