GOSPA-Driven Non-Myopic Multi-Sensor Management with Multi-Bernoulli Filtering

Abstract--In this paper, we propose a non-myopic sensor management algorithm for multi-target tracking, with multiple sensors operating in the same surveillance area. The algorithm is based on multi-Bernoulli filtering and selects the actions that solve a non-myopic minimisation problem, where the cost function is the mean square generalised optimal sub-pattern assignment (GOSPA) error, over a future time window. For tractability, the sensor management algorithm actually uses an upper bound of the GOSPA error and is implemented via Monte Carlo Tree Search (MCTS). The sensors have the ability to jointly optimise and select their actions with the considerations of all other sensors in the surveillance area. The benefits of the proposed algorithm are analysed via simulations. ENSOR management can be defined as the dynamic re-tasking of agile sensors to achieve an operational objective [1]. Sensors can be agile in a multitude of ways, from physically repositioning, changing direction or selecting a sensing mode. Myopic sensor management, sometimes called greedy sensor management, optimises the sensor resources for the immediate benefit of the system, not considering the long term effects of the actions being selected now. Non-myopic sensor management operates on the policy of considering these long-term effects of the actions selected now. Whilst it has an increased computational demand, non-myopic planning often produces more desirable results [2], [3].