Dynamic and Intelligent Sensor Tasking for Data Fusion, Target Acquisition, Tracking and Identification

Toyon proposes to develop algorithms that will dynamically task GMD sensors to support data fusion for improved acquisition, tacking and identification (ID) of time-critical ballistic targets. We treat the problem as a closed-loop feedback system where afusion-aided continuous ID process works with a dynamic sensor tasking (DST) module to maximize the information contained in the track database. The amount of information is quantified using an information-theoretic definition of the expected entropy inthe track database given a candidate sensor schedule. The domain of sensor tasks involves a range of different sensor modes so that the fusion and tracking (FT) module may best be serviced for different values of the expected signal-to-interference ratio,resolution, background clutter, drag coefficient, object classification and radar cross-section. In Phase I, Toyon will develop GMD sensor models within our SLAMEM(TM) simulation. We will then implement the DST module to control the new sensor models andevaluate the resulting performance. In Phase II, Toyon will improve the algorithm and the fidelity of the sensor models and will demonstrate the operation of the algorithm in real time for a scenario approved by the MDA. This could include participating ata future military exercise like Millennium Challenge 2002. The successful completion of this research will result in the development of a sensor manager that supports the improved acquisition, location and identification of ballistic targets. This will beuseful for many military sensor tasking and fusion and tracking applications in addition to civilian uses such as border patrol, counter drug and counter-terrorist operations, and environmental law enforcement. Furthermore, the models and algorithmsdeveloped under this effort can be implemented into the Hercules program as requested.