Performance prediction of quantized SAR ATR algorithms

Horvath, Matthew S.; Rigling, Brian D.

doi:10.1109/taes.2015.140717

Cited by 4 publications

(2 citation statements)

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“…, andp q,i is the scalar notation of the p i utilized in (5). By substituting a realized quantile, q r at pixel i, the penalty associated with a given realization of the class-conditional distribution at that pixel, t i , is realized.…”

Section: Mpm Test Statistic and Classification Decisionmentioning

confidence: 99%

“…In the case that the parameters of these class-conditional DM distributions can be written as an explicit function of an operating condition of interest, the performance can be approximated as a direct function of the operating condition. 5 Often an explicit parametrization is intractable and a more general approach can be used utilizing class-conditional parametrizations that vary as an implicit function of operating condition. Therefore, by training in-class templates as an operating condition is varied, we can predict the performance at each discrete setting, effectively sampling the performance curve.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Performance prediction of multinomial pattern matching under ideal point response variations

Horvath

Rigling

2016

Algorithms for Synthetic Aperture Radar Imagery XXIII

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Typical ATR performance metrics are based on the results of empirical studies on truthed datasets where it is difficult to fully sample the space of expected variation yielding potentially false generalizations of empirical performance results to a rigorous performance assessment. This is especially difficult when many sources of variation can exist in the data, typically referred to as operating conditions. Here, we propose a general method to analytically predict the classification performance of the MPM algorithm when samples are assumed realizations of two separate MPM template parametrizations differing as a function of a single, conditionally independent operation condition. This performance prediction approach is then used to investigate the role the ideal point response has in the classification performance of synthetic aperture radar targets. The exact trade-off we study is coherently processing an aperture to yield a single higher resolution image versus non-coherently processing the aperture to yield multiple lower resolution looks of a scene. Experiments are conducted using SAR imagery from the Air Force Research Laboratories Civilian Vehicle dataset. An additional performance analysis presents an analytic approach to predict algorithm performance under additive white Gaussian noise for a general N q allowing the performance loss under IPR variations to be mapped to an equivalent loss in signal-to-noise ratio.

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Section: Mpm Test Statistic and Classification Decisionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%