Paper
24 August 2000 Performance-complexity tradeoffs for several approaches to ATR from SAR images
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Abstract
The performance of an automatic target recognition (ATR) system for synthetic aperture radar (SAR) images is generally dependent upon a set of parameters which captures the assumptions made approximations made in the implementation of the system. This set of parameters implicitly or explicitly determines a level of database complexity for the system. A comprehensive analysis of the empirical tradeoffs between ATR performance and database complexity is presented for variations of several algorithms including a likelihood approach under a conditionally Gaussian model for pixel distribution, a mean squared error classifier on pixel dB values, and a mean squared error classifier on pixel quarter power values. These algorithms are applied under a common framework to identical training and testing sets of SAR images for a wide range of system parameters. Their performance is characterized both in terms of the percentage of correctly classified test images and the average squared Hilbert-Schmidt distance between the estimated and true target orientations across all test images. Performance boundary curves are presented and compared, and algorithm performance is detailed at key complexity values. For the range of complexity considered, it is shown that in terms of target orientation estimation the likelihood based approach under a conditionally Gaussian model yields superior performance for any given database complexity than any of the other approaches tested. It is also shown that some variant of each of the approaches tested delivers superior target classification performance over some range of complexity.
© (2000) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Joseph A. O'Sullivan and Michael D. DeVore "Performance-complexity tradeoffs for several approaches to ATR from SAR images", Proc. SPIE 4053, Algorithms for Synthetic Aperture Radar Imagery VII, (24 August 2000); https://doi.org/10.1117/12.396370
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Cited by 9 scholarly publications.
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KEYWORDS
Synthetic aperture radar

Detection and tracking algorithms

Databases

Automatic target recognition

Error analysis

Data modeling

Performance modeling

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