Adaptive multi-modality sensor scheduling for detection and tracking of smart targets

Abstract

This paper considers the problem of sensor scheduling for the purposes of detection and tracking of “smart” targets. Smart targets are targets that can detect when they are under surveillance and react in a manner that makes future surveillance more difficult. We take a reinforcement learning approach to adaptively schedule a multi-modality sensor so as to most quickly and effectively detect the presence of smart targets and track them as they travel through a surveillance region. An optimal scheduling strategy, which would simultaneously address the issue of target detection and tracking, is very challenging computationally. To avoid this difficulty, we use a two stage approach where targets are first detected and then handed off to a tracking algorithm. We investigate algorithms capable of choosing whether to use the active or passive mode of an agile sensor. The active mode is easily detected by the target, which makes the target prefer to move into hide mode. The passive mode is nearly undetectable to the target. However, the active mode has substantially better detection and tracking capabilities then the passive mode. Using this setup, we characterize the advantage of a non-myopic policy with respect to myopic and random polices for multitarget detection and tracking.