The public reporting burden for this coilection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, ABSTRACTDetection of an Unmanned Air Vehicle by radar is dependent on many variables including range, altitude, and relative orientation. Given a radar location and appropriate model for the likelihood of detection, a path plan can be created for an Unmanned Air Vehicle which constrains the probability of detection. In this paper such an approach is taken using a linearized detection model. The detection model and the Unmanned Air Vehicle's dynamics are represented as a linear program subject to mixed integer constraints. This mixed integer linear program (MILP) is then solved with commercial software which has been traditionally used by the Operations Research community. This approach searches for all feasible solutions and produces the best path plan based on the user specified parameters. Mstract-DetecAon of an Unmanned Air Vehicle by radar is dependent on many variables including range, altitude, and relative orientation. Given a radar location and appropriate . model for the lilielihood of detection, a path plan can be created for an Unmanned Air Vehicle which constrains the probability of detection. In this paper such an approach is taken using a linearized detection model. The detection model and the Unmanned Air Veliicle's dynamics are represented as a linear program subject to mixed integer constraints. Tliis mixed integer linear program (MILP) is then solved with commercial software which has l>een traditionally used by the Operations Research community. This approach searches for all feasible solutions and produces the best path plan based on the user specified parameters. SUBJECT TERMS

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This paper proposes a method for building with multiple vehicles a probability map of uncertain dynamic environments. It is assumed that each vehicle has a limited sensor range and therefore lacks global information. The vehicles share their measurement information to build a probability map. The probability map is updated using sensor information and a priori statistics of the dynamic environment.

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