GOSPA-Driven Non-Myopic Multi-Sensor Management with Multi-Bernoulli Filtering
For multi-target tracking with multiple sensors, this work addresses the challenge of non-myopic sensor management, offering a tractable solution with improved tracking accuracy.
This paper proposes a non-myopic multi-sensor management algorithm for multi-target tracking using multi-Bernoulli filtering, minimizing mean square GOSPA error over a future time window via Monte Carlo Tree Search. Simulations demonstrate benefits over myopic approaches.
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.