Simultaneous Distributed Sensor Self-Localization and Target Tracking Using Belief Propagation and Likelihood Consensus

We introduce the framework of cooperative simultaneous localization and tracking (CoSLAT), which provides a consistent combination of cooperative self-localization (CSL) and distributed target tracking (DTT) in sensor networks without a fusion center. CoSLAT extends simultaneous localization and tracking (SLAT) in that it uses also intersensor measurements. Starting from a factor graph formulation of the CoSLAT problem, we develop a particle-based, distributed message passing algorithm for CoSLAT that combines nonparametric belief propagation with the likelihood consensus scheme. The proposed CoSLAT algorithm improves on state-of-the-art CSL and DTT algorithms by exchanging probabilistic information between CSL and DTT. Simulation results demonstrate substantial improvements in both self-localization and tracking performance.Comment: 10 pages, 5 figure

Target Tracking in Confined Environments with Uncertain Sensor Positions

To ensure safety in confined environments such as mines or subway tunnels, a (wireless) sensor network can be deployed to monitor various environmental conditions. One of its most important applications is to track personnel, mobile equipment and vehicles. However, the state-of-the-art algorithms assume that the positions of the sensors are perfectly known, which is not necessarily true due to imprecise placement and/or dropping of sensors. Therefore, we propose an automatic approach for simultaneous refinement of sensors' positions and target tracking. We divide the considered area in a finite number of cells, define dynamic and measurement models, and apply a discrete variant of belief propagation which can efficiently solve this high-dimensional problem, and handle all non-Gaussian uncertainties expected in this kind of environments. Finally, we use ray-tracing simulation to generate an artificial mine-like environment and generate synthetic measurement data. According to our extensive simulation study, the proposed approach performs significantly better than standard Bayesian target tracking and localization algorithms, and provides robustness against outliers.Comment: IEEE Transactions on Vehicular Technology, 201

Multisensor Poisson Multi-Bernoulli Filter for Joint Target-Sensor State Tracking

In a typical multitarget tracking (MTT) scenario, the sensor state is either assumed known, or tracking is performed in the sensor's (relative) coordinate frame. This assumption does not hold when the sensor, e.g., an automotive radar, is mounted on a vehicle, and the target state should be represented in a global (absolute) coordinate frame. Then it is important to consider the uncertain location of the vehicle on which the sensor is mounted for MTT. In this paper, we present a multisensor low complexity Poisson multi-Bernoulli MTT filter, which jointly tracks the uncertain vehicle state and target states. Measurements collected by different sensors mounted on multiple vehicles with varying location uncertainty are incorporated sequentially based on the arrival of new sensor measurements. In doing so, targets observed from a sensor mounted on a well-localized vehicle reduce the state uncertainty of other poorly localized vehicles, provided that a common non-empty subset of targets is observed. A low complexity filter is obtained by approximations of the joint sensor-feature state density minimizing the Kullback-Leibler divergence (KLD). Results from synthetic as well as experimental measurement data, collected in a vehicle driving scenario, demonstrate the performance benefits of joint vehicle-target state tracking.Comment: 13 pages, 7 figure

A Sensor Self-aware Distributed Consensus Filter for Simultaneous Localization and Tracking

This is the author accepted manuscript. The final version is available from Springer via the DOI in this recordBackground/Introduction: Simultaneous localization and tracking (SLAT) has become a very hot topic in both academia and industry for its potential wide applications in robotic equipment, sensor networks and smart devices. In order to exploit the advantages supported by state filtering and parameter estimation, researchers have proposed adaptive structures for solving SLAT problems. Existing solutions for SLAT problems that rely on belief propagation often have limited accuracy or high complexity. To adapt the brain decision mechanism for solving SLAT problems, we introduce a specific framework that is suitable for wireless sensor networks. Methods: Motivated by the high efficiency and performance of brain decision making built upon partial information and information updating, we propose a cognitively distributed SLAT algorithm based on an adaptive distributed filter, which is composed of two stages for target tracking and sensor localization. The first stage is consensus filtering that updates the target state with respect to each sensor. The second stage employs a recursive parameter estimation that exploits an on-line optimization method for refining the sensor localization. As an integrated framework, each consensus filter is specific to a separate sensor subsystem and gets feedback information from its parameter estimation. Results: The performance comparison in terms of positioning accuracy with respect to RMSE is shown and the simulation results demonstrate that the proposed ICF-RML performs better than the BPF-RML. This is expected since the distributed estimation with sufficient communication mechanism often achieves higher accuracy than that of less sufficient cases. Furthermore, the performance of the ICF-RML is comparable with that of the BPF-RML even if the latter assumes known prior network topology. We also observe from the results of tracking errors that ICF-RML accomplishes a remarkable improvement in the precision of target tracking and achieves more stable convergence than BPF-RML, in the scenario that all sensors are used to calculate the effect from data association errors. Conclusion: We apply this approach to formulate the SLAT problem and propose an effective solution, summarized in the paper. For small-size sensor networks with Gaussian distribution, our algorithm can be implemented through a distributed version of weighted information filter and a consensus protocol. Comparing the existing method, our solution shows a higher accuracy in estimation but with less complexity.National Natural Science Foundation of ChinaShandong Provincial Natural Science FoundationShandong Outstanding Young Scientist FundRoyal SocietyFundamental Research Funds for the Central Universitie

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

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