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This paper deals with mobile multi-target detection and tracking. In the traditional method, there are uncertainties such as misdetection and false alarm in the measurement data, and it will be inevitable having to deal with the data association. To solve the target trajectory and state estimation problem under a cluttered environment, this paper proposes a non-concurrent multi-target acoustic localization tracking method based on the Gibbs-generalized labelled multi-Bernoulli (Gibbs-GLMB) filter and considers an acoustic array of a fixed arrangement for the tracking of targets by joint time difference of arrival (TDOA) and angle of arrival (AOA) measurements. Firstly, the TDOAs are calculated by using the generalized cross-correlation algorithm (GCC) and the AOAs are derived from the received signal directions. Secondly, we assume the independence of the targets and fuse the measurements which are used to track the multiple targets via the Gibbs-GLMB filter. Finally, the effectiveness of the method is verified by Monte Carlo simulation experiments.

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In the field of resolvable group target tracking, further study on the structure and formation of group targets is helpful to reduce the tracking error of group bluetargets. In this paper, we propose an algorithm to detect whether the structure or formation state of group targets changes. In this paper, a Gibbs Generalized Labeled Multi-Bernoulli (GLMB) filter is used to obtain the estimation of discernible group target bluestates. After obtaining the state estimation of the group target, we extract relevant information based on the estimation data to judge whether the structure or formation state has changed. Finally, several experiments are carried out to verify the algorithm.

This article focuses on H ∞ containment control and the communication network topologies that are driven by a semi‐Markov chain. Moreover, the communication channels between agents exist time‐varying delays and noise. Firstly, the authors extend the Markov switching topologies to semi‐Markov switching topologies. Because the transition rate of the semi‐Markov switching topology is time‐varying and depends on the sojourn time, the analysis of containment control under semi‐Markov switching topology becomes more challenging. Secondly, a control protocol with time‐varying delays is adopted. The error function is derived by the property of graph theory, convex hull and communication noise. Hence, the problem of H ∞ containment control is transformed into the stability problem of the semi‐Markov jump system. To avoid the zero initial condition in the traditional H ∞ control approach, a novel performance function is constructed with the initial condition considered. Finally, simulation experiments are provided to verify the effectiveness of the proposed algorithm.