Automatic Detection of Radar Signature Defects,
NAVAL AIR WARFARE CENTER WEAPONS DIV CHINA LAKE CA
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Field-level maintenance of radar signature treatment requires that non-specialist military personnel properly identify needed repairs. To simplify this task, an automated method is required that can compare radar signature data to baseline data, measure the differences, and identify the source of serious defects. Significant work has been done using artificial intelligence AI techniques to simplify this diagnostic task. A portable measurement radar was used to gather signature data on a small MQM-107D target drone. One set of data was collected of a baseline vehicle. Then data was collected after several anomalies were introduced, such as an uncovered pitot tube, wing joint untaped, or fastener screw not tightened. The data was processed as global downrange plots, and then baseline data was subtracted from anomaly data and the difference was compared to signature specifications as a function of angle. AI was used to identify signature defects that require repair. The results showed that an AI-aided diagnostic tool could help identify places where signature treatment repair was needed. This tool can be adapted to a variety of user and target needs.
- Active and Passive Radar Detection and Equipment