This letter addresses the sensor selection problem for tracking multiple dynamic targets within a sensor network. Since the bandwidth and energy of the sensor network are constrained, it would not be feasible to directly use the entire information of sensor nodes for detection and tracking of the targets and hence the need for sensor selection. Our sensor selection solution is formulated using the multi-Bernoulli random finite set framework. The proposed method selects a minimum subset of sensors which are most likely to provide reliable measurements. The overall scheme is a robust method that works in challenging scenarios where no prior information are available on clutter intensity or sensor detection profile. Simulation results demonstrate successful sensor selection in a challenging case where five targets move in a close vicinity to each other. Comparative results show the superior performance of our method in terms of accuracy of estimating the number of targets and their states.

This paper presents a new solution for multi-target tracking over a network of sensors with limited spatial coverage. The proposed solution is based on the centralized data fusion architecture. The main contribution of the paper is the introduction of a new track-to-track fusion approach in which the posterior distributions of multi-target states, reported by various sensor nodes, are fused in a way that the redundant information are combined and the rest complement each other. The proposed solution is formulated within the labeled random finite set framework in which the fused posterior incorporates all the state and label information provided by multiple sensor nodes. The performance of the proposed method is evaluated via simulation experiments that involve challenging tracking scenarios. The proposed method is implemented using sequential Monte Carlo method and the results confirm its effectiveness.

When multiple robots are involved in the process of simultaneous localization and mapping (SLAM), a global map should be constructed by merging the local maps built by individual robots, so as to provide a better representation of the environment. Hence, the map-merging methods play a crucial rule in multi-robot systems and determine the performance of multi-robot SLAM. This paper looks into the key problem of map merging for multiple-ground-robot SLAM and reviews the typical map-merging methods for several important types of maps in SLAM applications: occupancy grid maps, feature-based maps, and topological maps. These map-merging approaches are classified based on their working mechanism or the type of features they deal with. The concepts and characteristics of these map-merging methods are elaborated in this review. The contents summarized in this paper provide insights and guidance for future multiple-ground-robot SLAM solutions.

This paper presents a new sensor management method for multitarget filtering, that is designed based on maximizing a measure of confidence in accuracy of the multitarget state estimate. Confidence of estimation is quantified by optimal subpattern assignment-based dispersion of the multitarget posterior about its statistical mean. Implementation of the algorithm for generic multitarget filters is presented. Simulation studies with labeled multi-Bernoulli filter demonstrate excellent performance in challenging sensor control scenarios.

A constrained sensor control method is presented for multiobject tracking using labeled multi-Bernoulli filters. The proposed framework is based on a novel approximation of the Cauchy-Schwarz divergence between the labeled multi-Bernoulli prior and posterior densities, which does not need Monte Carlo sampling of random sets in the multiobject space. The void probability functional is also formulated for labeled multi-Bernoulli distributions and used within our proposed method to form a constrained sensor control solution. Numerical studies demonstrate that reasonably acceptable movements are decided for the controlled sensor by our sensor control method, with the advantage that the void probability constraint is formally considered as part of the sensor control optimization algorithm.