An Active Sonar Signal Detection Algorithm Based on Weighted Neighbourhood Relative Entropy in the Underwater Acoustic Noise Environment

Abstract

This paper presents a target detection method for active sonar based on weighted neighbourhood relative entropy (WNRE), using tools from information geometry. The method segments the matched-filter output of each echo by a sliding window, estimates the probability density of every window by kernel density estimation, and forms the detection statistic from the Jensen–Shannon divergence between each window and its Gaussian-weighted neighbourhood, so that no pre-stored noise template or prior knowledge of the noise distribution is required. Semi-physical experiments on deep-sea active sonar data from the South China Sea show that the weighted Jensen–Shannon divergence (W-JS) detector reaches Pd = 0.985 at Pfa = 0.01 at an input SNR of 9 dB on the deep-sea near-Gaussian convergent zone background, where it requires about 2.7 dB less input SNR than the CA-CFAR detector to attain Pd = 0.5. The method is most effective in near-Gaussian, locally stationary convergent-zone backgrounds; in strongly reverberant and non-stationary regions its advantage diminishes, where energy-based constant false-alarm-rate (CFAR) detectors regain competitiveness. Operating at the window level rather than point-by-point, and at the same per-decision false-alarm probability, it yields fewer expected false-alarm events per scan cycle than conventional point-by-point detectors.