Threat-Based Sensor Management For Multi-target Tracking

Abstract

Multi-sensor management for multi-target tracking is a theoretically and computationally challenging problem. A multi-sensor management algorithm is proposed within the partially observed Markov decision process (POMDP) framework, and cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter is applied to track targets. The novelties lie in evaluating the threat degree of the targets using the analytic hierarchy process (AHP) and the threat-based sensor selection algorithm. Considering distance, speed and heading of targets, we use the AHP to evaluate the threat degree of targets at each sampling time. Based on the threat level of targets and detection distance of sensors, the sensor-target matching algorithm is proposed. Numerical studies are presented in the dynamical system. The simulation results show the feasibility of the method.