Sequential detection using higher-order statistics

The binary hypothesis testing problem is formulated in the higher order statistics (HOS) domain, with the advantages that the test is insensitive to additive Gaussian noise of unknown covariance and insensitive to signal shifts. Unlike detection with a matched filter, the asymptotically maximum likelihood HOS-based test requires no prewhitening and no synchronization. The asymptotic normality of the test statistic is exploited to formulate three sequential detection tests: a fixed sample size test, a sequential probability ratio test (SPRT), and a truncated sequential test. The SPRT performance is examined through the average sample number by comparing a large sample analytical expression with a Monte Carlo experiment. The results validate the test assumptions