Improved underwater signal detection using efficient time–frequency de-noising technique and Pre-whitening filter

Abstract

Optimal signal detection is important in sonar and underwater digital communication. However, a detailed knowledge of the statistics of the noise present is required to achieve optimal signal detection. Additive white Gaussian noise (AWGN) is assumed in many applications; thus, a linear correlator (LC), which is known to be optimal in the presence of AWGN, is normally used. However, underwater acoustic noise (UWAN) influences the reliability of signal detection in applications, in which the noise is non-white and non-Gaussian. As a result, an LC detector performs poorly in underwater applications. Accordingly, the Gaussian noise injection detector (GNID) is proposed in this study to improve detection probability (PD) based on a noise-enhanced signal detection using a pre-whitening filter, a time–frequency de-noising method based on S-transform, and an inverse whitening filter. Sea-truth data are collected at Desaru Beach on the eastern shore of Johor in Malaysia using broadband hydrophones. These data are used as UWAN to validate the proposed method. The performances of four different detectors, namely, the proposed GNID, a locally optimal (LO) detector, a sign correlation (SC) detector, and a conventional LC detector, are evaluated according to their PD values. Given a false alarm probability of 0.01 and PD value of 90%, the energy-to-noise ratios of the GNID are better than the LO, SC, and LC detectors by 1.77, 3.81, and 3.61 dB, respectively, for a time-varying signal.