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A Suboptimal Multi-Sensor Management Based on Cauchy-Schwarz Divergence for Multi-Target Tracking

In this paper, we address the problem of multisensor management for multi-target tracking via labeled random finite sets (LRFS) in sensor network systems which require both precision and real-time. Considering the optimal multisensor management strategy (proposed in [1] named joint decision making (JDM) algorithm) suffers from the high-dimensional computational complexity, to compromise between tractability and fidelity, an alternative multi-sensor management strategy is proposed. By sequentially calculating the Cauchy-Schwarz (CS) divergence between global generalized Covariance Intersection (GCI) fusion result and the GCI fusion result of two sensors, the JDM algorithm is simplified as a hybrid decision making with a two-dimensional optimization problem, which is referred to as the HDM algorithm, and meanwhile the proposed HDM algorithm is superior to the independent decision making (IDM) algorithm [1] in precision due to the IDM algorithm completely ignores the correlation among sensors. The computational complexity of the proposed method is also provided by comparison with the JDM and IDM algorithms. The efficiency as well as the performance of the proposed method is well demonstrated in a challenging multisensor multi-target tracking scenario by numerical results.