Dual control inspired active sensing for bearing-only target tracking

Automating sensing processes is of high interest to both the target tracking and the control community. Active sensing is focused on solving this task, usually with information based or task driven selection of optimal sensing actions. This paper presents an active sensing formulation that combines task based, in the form of standoff tracking, and information based active sensing by implementing the dual control for exploitation and exploration (DCEE) concept to control a mobile sensor platform with a limited field-of-view. The DCEE based cost function is integrated into the Monte Carlo tree search (MCTS) framework for non-myopic decision making. Using the Bernoulli particle filter for single target tracking with bearing-only measurements, the DCEE observer control method is benchmarked against the popular Rényi divergence information metric with two different parameterisations. Whilst the Rényi divergence performs marginally better when considering existence estimation, spatial results clearly demonstrate that our formulation is able to outperform the benchmark algorithm with improved target localisation performance resulting from outmanoeuvring of the target.