Multi-bernoulli sensor control for multi-target tracking

A new approach to solve the sensor control problem is proposed, formulated based on multi-object Bayes filtering in the partially observable Markov decision process (POMDP) context, where the multi-object states are assumed to be random finite sets with multi-Bernoulli distributions. We introduce a novel cost function that is reliable in real-time environment. In each filtering iteration, after predicting the multi-Bernoulli parameters, estimates for the number and states of the targets are extracted. For each admissible control command, Monte-Carlo samples of measurements corresponding to the estimated target states are generated. Then, for each measurement sample, the CB-MeMBer update is performed and the average cost function is computed. The best command is the one incurring the minimum cost. The simulation results involve a challenging case of detecting and tracking up to 5 manoeuvring targets using a controllable sensor, and show that our method outperforms competing methods both in terms of tracking accuracy (measured in using OSPA metric) and in terms of computational cost.