Guidance Laws for Partially-Observable Interception Based on Linear Covariance Analysis

Abstract

We consider pursuit-evasion games in which the pursuer is tasked with intercepting the evader using only partial measurements. Motivated by the utilization of visual sensing on board the pursuer, we focus on the case when only bearing measurements are available to the pursuer. The resulting partially-observable interception problem is computationally challenging, and the separation principle does not hold in general. In this paper, we identify a set of maneuvers that improve observability, and we propose an algorithm that utilizes these maneuvers to move the pursuer so that the expected payoff of the differential game is maximized. The algorithm uses in-the-loop uncertainty propagation based on linear covariance analysis to assess the effect of the maneuvers. We evaluate the resulting guidance law in experiments involving a quadcopter in flight representing the pursuer, and a simulated evader.