Towards integrated threat assessment and sensor management: Bayesian multi-target search

Currently, most land intelligence, surveillance and reconnaissance (ISR) systems, especially those employed in critical infrastructure protection contexts, comprise of a suite of sensors (e.g. EO/IR, radar, etc.) loosely integrated into a central command and control (C2) system with limited autonomy. We consider a concept of a modular and autonomous architecture where a set of heterogeneous autonomous sensor modules (ASMs) connect to a high-level decision making module (HLDMM) in a plug and play manner. Working towards an integrated threat evaluation and sensor management approach which is capable of optimizing the ASM suite to search for, localise, and capture relevant imagery of multiple threats in and around the area under protection, we propose a Bayesian multi-target search algorithm. In contrast to earlier work we demonstrate how the algorithm can reduce the time to acquire threats through incorporation of target dynamics. The derivation of the algorithm from an information-theoretic perspective is given and its links with the probability hypothesis density (PHD) filter are explored. We discuss the results of a demonstration HLDMM system which embodies the search algorithm and was tested in realistic base protection scenarios with live sensors and targets.