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Proceedings Paper

Using phase for radar scatterer classification
Author(s): Linda J. Moore; Brian D. Rigling; Robert P. Penno; Edmund G. Zelnio
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Paper Abstract

Traditional synthetic aperture radar (SAR) systems tend to discard phase information of formed complex radar imagery prior to automatic target recognition (ATR). This practice has historically been driven by available hardware storage, processing capabilities, and data link capacity. Recent advances in high performance computing (HPC) have enabled extremely dense storage and processing solutions. Therefore, previous motives for discarding radar phase information in ATR applications have been mitigated. First, we characterize the value of phase in one-dimensional (1-D) radar range profiles with respect to the ability to correctly estimate target features, which are currently employed in ATR algorithms for target discrimination. These features correspond to physical characteristics of targets through radio frequency (RF) scattering phenomenology. Physics-based electromagnetic scattering models developed from the geometrical theory of diffraction are utilized for the information analysis presented here. Information is quantified by the error of target parameter estimates from noisy radar signals when phase is either retained or discarded. Operating conditions (OCs) of signal-tonoise ratio (SNR) and bandwidth are considered. Second, we investigate the value of phase in 1-D radar returns with respect to the ability to correctly classify canonical targets. Classification performance is evaluated via logistic regression for three targets (sphere, plate, tophat). Phase information is demonstrated to improve radar target classification rates, particularly at low SNRs and low bandwidths.

Paper Details

Date Published: 3 May 2017
PDF: 20 pages
Proc. SPIE 10201, Algorithms for Synthetic Aperture Radar Imagery XXIV, 102010J (3 May 2017); doi: 10.1117/12.2267832
Show Author Affiliations
Linda J. Moore, Air Force Research Lab. Sensors Directorate (United States)
Univ. of Dayton (United States)
Brian D. Rigling, Wright State Univ. (United States)
Robert P. Penno, Univ. of Dayton (United States)
Edmund G. Zelnio, Air Force Research Lab. Sensors Directorate (United States)


Published in SPIE Proceedings Vol. 10201:
Algorithms for Synthetic Aperture Radar Imagery XXIV
Edmund Zelnio; Frederick D. Garber, Editor(s)

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