A class of sequential adaptive detectors for underwater non-gaussian noise environments

The problem of sequential signal detection in non-Gaussian noise is considered. The proposed detectors arc robust and increase the transmission rate up to four times the rate of their fixed-sample-size counterparts. The performance of the detectors is evaluated for low signal-to-noise-ratio (SNR) environments encountered in underwater acoustics. To improve the efficiency and to suppress the influence of large samples, truncated and curved decision boundaries are introduced at the detector.