When the Palomares accident took place, I never imagined they used bayesian search theory to find the last empty quiver. According to Wikipedia it so happens that this technique is used by the Coast Guards in search and rescue operations. A similar technique could be used to merge all the information acquired to find Jim Gray.
The search and rescue program seems to now use SAROPS ( a commercial version is SARMAP) where I would expect similar bayesian techniques to be used. I found the other presentation at this SAR conference at Ifremer also very much interesting. The 2006 presentations also present the current SAROPS implementation (link to movie/presentation).
I want to be proven wrong, but I am pretty sure that current tools do not integrate satellite/radar imagery directly into the maps produced to determine search scenarios. It certainly does not integrate other types of imagery (multispectral or other). I would very much be interested in finding out how time is taken into account in these probabilistic maps.
Unlike other approaches, the Bayesian approach maintains multiple hypotheses over time. The probabilistic maps developed for robotics are sensibly similar to the ones needed in the search and rescue case.
[Thanks Cable for the tip]
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