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Proceedings Paper

Performance evaluation of SAR/GMTI algorithms
Author(s): Wendy Garber; William Pierson; Ryan Mcginnis; Uttam Majumder; Michael Minardi; David Sobota
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Paper Abstract

There is a history and understanding of exploiting moving targets within ground moving target indicator (GMTI) data, including methods for modeling performance. However, many assumptions valid for GMTI processing are invalid for synthetic aperture radar (SAR) data. For example, traditional GMTI processing assumes targets are exo-clutter and a system that uses a GMTI waveform, i.e. low bandwidth (BW) and low pulse repetition frequency (PRF). Conversely, SAR imagery is typically formed to focus data at zero Doppler and requires high BW and high PRF. Therefore, many of the techniques used in performance estimation of GMTI systems are not valid for SAR data. However, as demonstrated by papers in the recent literature,1-11 there is interest in exploiting moving targets within SAR data. The techniques employed vary widely, including filter banks to form images at multiple Dopplers, performing smear detection, and attempting to address the issue through waveform design. The above work validates the need for moving target exploitation in SAR data, but it does not represent a theory allowing for the prediction or bounding of performance. This work develops an approach to estimate and/or bound performance for moving target exploitation specific to SAR data. Synthetic SAR data is generated across a range of sensor, environment, and target parameters to test the exploitation algorithms under specific conditions. This provides a design tool allowing radar systems to be tuned for specific moving target exploitation applications. In summary, we derive a set of rules that bound the performance of specific moving target exploitation algorithms under variable operating conditions.

Paper Details

Date Published: 14 May 2016
PDF: 14 pages
Proc. SPIE 9843, Algorithms for Synthetic Aperture Radar Imagery XXIII, 984305 (14 May 2016); doi: 10.1117/12.2230385
Show Author Affiliations
Wendy Garber, Matrix Research Inc. (United States)
William Pierson, Matrix Research Inc. (United States)
Ryan Mcginnis, Matrix Research Inc. (United States)
Uttam Majumder, Air Force Research Lab. (United States)
Michael Minardi, Air Force Research Lab. (United States)
David Sobota, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 9843:
Algorithms for Synthetic Aperture Radar Imagery XXIII
Edmund Zelnio; Frederick D. Garber, Editor(s)

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