Path Planning for Multi-Platform Bearings-Only Tracking in the Possibilistic Framework

This paper presents a novel approach to path planning for multi-platform bearings-only tracking of a target in the presence of epistemic detection uncertainty. Instead of using the traditional probabilistic framework, we propose a solution with a coordinated intelligent sensor platform motion control strategy in the possibilistic framework, offering a viable and robust alternative for improved tracking performance. We use track fusion of possibilistic Bernoulli filter implemented with Gaussian-max models to integrate information gathered by multiple platforms. The reward function for intelligent platform motion control is constructed using the concept of possibilistic entropy. The tracking performance of the proposed solution is evaluated using selected metrics via simulations.