Abstract
The authors propose robust detection schemes for detecting signals corrupted by additive non-Gaussian noise by employing an order statistic filter (OSF) as a preprocessor. The OSF can effectively suppress non-Gaussian noise components, but its output characteristics are not easy for mathematical manipulation due to its nonlinear operation. To alleviate difficulty in the analytical design of the detector, the output variance of the OSF is approximated by a piecewise linear model. The sequential detectors are designed using the sequential probability ratio test (SPRT) and truncated SPRT (TSPRT) schemes. The performance of the proposed detectors is compared to that of other robust detectors in terms of the sample size required for given false alarm and miss detection probabilities. Finally, analytical results are verified by computer simulation.
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