Probabilistic shadow information spaces

This paper introduces a Bayesian filter that is specifically designed for counting targets that move outside of the field of view while performing a sensor sweep. Information space concepts are used to dramatically reduce the filter complexity so that information is processed only when the shadow region (all points invisible to the sensors) changes combinatorially or targets pass in and out of view. Previous work assumed perfect observations; however, this paper extends the approach to enable probabilistic disturbances. Practical algorithms are introduced, implemented, and demonstrated for computing the filter outputs based on realistic data.