Coordinated search for a lost target in a Bayesian world

Abstract

This paper describes a decentralized Bayesian approach to the problem of coordinating multiple autonomous sensor platforms searching for a single non-evading target. In this architecture, each decision maker builds an equivalent representation of the probability density function (PDF) of the target state through a general decentralized Bayesian sensor network, enabling them to coordinate their actions without exchanging any information about their plans. The advantage of the approach is that a high degree of scalability and real-time adaptability can be achieved. The framework is implemented on a real-time high-fidelity multi-vehicle simulator system. The effectiveness of the method is demonstrated in different scenarios for a team of airborne search vehicles looking for both a stationary and a drifting target lost at sea.