The comparison of underwater source localization between Riemannian MFP and blind channel equalizer

Abstract

Blind channel equalization (BCE) has been widely used in underwater communications due to its strong robustness against multipath propagation and its suitability for rapidly varying environments. However, there has been little research on the application of BCE for underwater source localization. On the other hand, conventional matched field processing (MFP), and particularly Riemannian MFP (RMFP), have been regarded as highly effective for this problem. In this paper, based on the statistical characterization of the signal-to-noise ratio (SNR) in underwater acoustic channels, we propose a method for estimating the channel transfer function, which is then used to construct a blind channel equalizer. A source localization approach using the proposed BCE is also presented. The localization performance using BCE is comparable to that of RMFP, achieving a depth error of 10 meters and a range error of 100 meters, while requiring significantly lower computational complexity.