Paper
13 August 1999 Peak stability derived from phase history in synthetic aperture radar
Warren E. Smith, Theodore Irons, Jay Riordan, Steven Sayre
Author Affiliations +
Abstract
The stability of bright peaks over the complex phase history of synthetic aperture radar (SAR) imagery is examined using a 'sub-aperture imaging' approach. The additional information obtained about the peaks can be applied to the following areas: improved peak matching in the automatic target recognition (ATR) problem; false-alarm determination; and identification of imaging artifacts, such as the sidelobes of bright peaks. To estimate peak-stability information, the complex phase history of the SAR image is broken up into a series of overlapping cross-range windows from which the peak information is extracted. The change in the peak characteristics from one window to the next is correlated to produce information related to the change in the amplitude of the peak over the entire aperture and also to estimate small motions of the peak over the aperture. These changes in peak characteristics are due to various phenomena as the synthetic aperture is swept out: scatterers interfere with each other, scatterer sources are created and destroyed, and aspect- dependent scatterers are interrogated differently with angle. This stability analysis has been incorporated into the online MSTAR system's Feature Extraction module. The products produced for the MSTAR system will be described. Applications of this method to the ATR problem will be discussed.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Warren E. Smith, Theodore Irons, Jay Riordan, and Steven Sayre "Peak stability derived from phase history in synthetic aperture radar", Proc. SPIE 3721, Algorithms for Synthetic Aperture Radar Imagery VI, (13 August 1999); https://doi.org/10.1117/12.357661
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CITATIONS
Cited by 6 scholarly publications.
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KEYWORDS
Synthetic aperture radar

Sensors

Automatic target recognition

Target detection

Scattering

Signal to noise ratio

Feature extraction

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