Signal detection and classification using matched filtering and higher order statistics

A method for signal detection and classification in the presence of additive Gaussian noise using higher-than-second-order statistics of the matched filter output is presented. Deterministic and random, nonGaussian distributed signals are detected via multiple correlations and cumulants, respectively. The detection algorithm is computationally simple, and, contrary to standard matched filtering, it is insensitive to signal shifts and does not require knowledge of the noise spectrum for prewhitening. The detector can be viewed as a likelihood radio test between sampled higher-order statistics, and its performance is evaluated using binary hypothesis testing. Signals are designed to have equal higher-order correlation energies and then classified based on higher-order statistics. Two-dimensional extensions of the one-dimensional algorithms are discussed briefly. Simulations illustrate successful performance of the detection and classification algorithms at low signal-to-noise ratio.<
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