3-D source localization in shallow ocean with non-Gaussian noise using a linear array of acoustic vector sensors

This paper presents a computationally efficient method of 3-D localization of underwater acoustic sources in heavy-tailed non-Gaussian noise by an acoustic vector sensor (AVS) array. Noise is modeled as a Gaussian mixture. Estimation of source coordinates, signal waveform and noise parameters is based on the space-alternating generalized EM (SAGE) algorithm. This method requires one 2-D search and an iterative sequence of 1-D searches in contrast to the conventional ML estimation which requires a more computation-intensive 3-D search. It is shown that the proposed method performs better than the conventional subspace-based method and that an AVS vertical array provides a significantly better performance than an AVS horizontal array.