Exploiting Bounded Sensor Field-of-View Geometry in Tracking and Sensor Planning Problems

In search-detect-track problems, knowledge of where objects were not seen can be as valuable as knowledge of where objects were seen. Exploiting the sensor's known sensing extents, or field-of-view (FoV), this type of evidence can be incorporated in a Bayesian framework to improve tracking accuracy and form better sensor schedules. This paper presents new techniques for incorporating bounded FoV inclusion/exclusion evidence in object state densities and multi-object cardinality distributions. Some examples of how the proposed techniques may be applied to tracking and sensor planning problems are given.