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22 October 2001 ATR discrimination-SNR for HRR assuming χ2 model of RCS variability
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A discrimination SNR for predicting classification performance is developed as an analogy to the RADAR equation that is used to predict detection performance. It assumes a statistical model for the target radar cross-section (RCS) and that the resulting likelihood classifier is employed. The relationship between the probability of classification errors and the dB value of the discrimination SNR is obtained. A specific form for the likelihood classifier and the discrimination SNR is developed assuming that the variability of the target RCS is described by a (chi) 2 - distribution. The form of this (chi) 2 - based classifier is novel and significantly different from the more common Gaussian based mean-square-error classifier. It is shown that the discrimination SNR has an intuitive interpretation in terms of the number of radar samples, the average contrast between targets and the contrast-noise. The use of this tool is illustrated using compact range High Range Resolution (HRR) Doppler measurements from the U.S. Army National Ground Intelligence Center (NGIC). The sensitivity of ATR performance to radar parameters is quantified using the discrimination SNR with gains measured in meaningful dB units.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Craig R. Holt, Steven L. Schmidt, and Joseph B. Attili "ATR discrimination-SNR for HRR assuming χ2 model of RCS variability", Proc. SPIE 4379, Automatic Target Recognition XI, (22 October 2001);

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