Abstract: For electronic countermeasures and dense clutter environments, a sensor management algorithm based on decision uncertainty using the measurement-driven multi-target filter is proposed. First, according to the theory of partially observable Markov decision process, a general sensor management approach based on Rényi divergence is presented. Meanwhile, taking into account the information integrity, information quality and information connotation in the decision-making process, we evaluate the multi-target decision uncertainty level based on the target motion situation in the measurement-driven adaptive filtering framework, subsequently selecting the maximum decision uncertainty target. Finally, the sensor allocation scheme is solved with the maximum information gain of the maximum decision uncertainty target as the criterion. The simulation results show that the proposed algorithm can effectively suppress the influence of electronic countermeasures and dense clutter on multi-target tracking and sensor management. Compared with the threat-based sensor management algorithm, the average Optimal Sub-Pattern Assignment (OSPA) distance and the average calculation time are significantly reduced. In cases of dense clutter and electronic countermeasures, the proposed algorithm has high reliability.

Key words: sensor management, multi-target tracking, tactical significance map, measurement-driven, partially observable Markov decision process