Abstract

Most radar systems employ a feed-forward processing chain in which they first perform some low-level processing of received sensor data to obtain target detections and then pass the processed data on to some higher-level processor such as a tracker, which extracts information to achieve a system objective. System performance can be improved using adaptation between the information extracted from the sensor/processor and the design and transmission of subsequent illuminating waveforms. As such, cognitive or fully adaptive radar systems offer much promise. In this paper, we develop a general fully adaptive radar framework for a radar system engaged in target tracking. The model includes the higher-level tracking processor and specifies the feedback mechanism and optimization criterion used to obtain the next set of sensor data. Performance is demonstrated on a distributed sensor system in which system resources (observation time on each sensor) are allocated to optimize single target tracking performance.

Full Text
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