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Proceedings Paper

Detector robustness to change in depression angle
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Paper Abstract

Many ultra-wideband (UWB) synthetic aperture radar (SAR) detection agorithms employ some combination of a set of features, calculated from the incoming raw radar data return, to segregate targets from clutter in a SAR image. Based on the training data, the algorithm designer selects those features that exploit some difference in the physical characteristics between the target class and clutter class. A detection algorithm is then trained to determine values for a set of algorithm parameters that will minimize some sort of error criterion. The physical characteristics that guide the feature selection can change, however, with changes in the attributes of the data collection, such as the depression angle from the radar to the point of interest. When the depression angle changes, the algorithm parameters that were optimal for the training data may no longer be optimal for test data at a different depression angle. We examine the changes in detector performance resulting from depression angle mismatches between the training and test data sets.

Paper Details

Date Published: 30 July 2002
PDF: 12 pages
Proc. SPIE 4744, Radar Sensor Technology and Data Visualization, (30 July 2002); doi: 10.1117/12.488276
Show Author Affiliations
Getachen Kirose, Army Research Lab. (United States)
Kenneth I. Ranney, Army Research Lab. (United States)
Chi Tran, Army Research Lab. (United States)

Published in SPIE Proceedings Vol. 4744:
Radar Sensor Technology and Data Visualization
Nickolas L. Faust; Nickolas L. Faust; James L. Kurtz; Robert Trebits, Editor(s)

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