Bayesian Search and Rescue

While some people are still looking for Jim Gray there are a number of issues that eventually need to be investigated at the Science and Technology level. While the current SAROPS capabilities of the Coast Guards are very impressive, there may be ways to improve some of its already powerful capabilities. I recently came across a technique that could probably have helped in solving the Tenacious Challenge. It is entitled: Coordinated Decentralized Search for a Lost Target in a Bayesian World [1] by Frederic Bourgault, Tomonari Furukawa and Hugh F. Durrant-Whyte. The abstract reads as follows:

This paper describes a decentralized Bayesian approach to coordinating multiple autonomous sensor platforms searching for a single non-evading target. In this architecture, each decision maker builds an equivalent representation of the target state PDF through a Bayesian DDF network enabling him or her 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 effectiveness of the approach is demonstrated in different scenarios by implementing the framework for a team of airborne search vehicles looking for a stationary, and a drifting target lost at sea.

and looks like the type of approach I was mentioning earlier. This one article takes as a starting point the tragedy of the 1979 Fastnet race. As it turns out, another Tenacious won that race. I will be sharing my thoughts on this technique and other possible improvements in a future entry. A related subject of interest is sensor networks since we had a mix of different sensors watching the same areas at different times.

[1] Intelligent Robots and Systems, 2003. (IROS 2003). Proceedings. 2003 IEEE/RSJ International Conference on. Publication Date: 27-31 Oct. 2003, Volume: 1, On page(s): 48- 53 vol.1.