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1 September 1995 Discrimination gain to optimize detection and classification
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A method for managing agile sensors to optimize detection and classification based on discrimination gain is presented. Expected discrimination gain is used to determine threshold settings and search order for a collection of discrete detection cells. This is applied in a low signal-to-noise environment where target-containing cells must be sampled many times before a target can be detected or classified with high confidence. Bayes rule is used to compute the expected discrimination gain for each sample region using estimated probability that it contains a target. This gain is used to select the optimal cell for the next sample. The effectiveness of this approach was assessed in a simple test case by comparing the result of discrimination optimized search with direct search. For a single 0 dB Gaussian target, the error rate for discrimination optimized search was similar to the direct search result against a 6 dB target.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Keith D. Kastella "Discrimination gain to optimize detection and classification", Proc. SPIE 2561, Signal and Data Processing of Small Targets 1995, (1 September 1995);

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