Underwater DOA Estimator Based on Variational Bayesian Inference with Non-uniform Noise
Yongfeng Huang, Zhendong Chen, Dingzhao Li, Haixin Sun · 2024
In complex underwater communication environments, Gaussian white noise models often fail to capture the true characteristics of the ocean environment accurately. Especially for low-frequency signals, the relative increase in array aperture leads to differences in the noise variance received between array elements, resulting in a decrease in the precision of the direction of arrival (DOA) estimator. Therefore, we innovatively propose a non-uniform noise Bayesian framework to address this challenge. Within this framework, the received signal is regarded as a synthesis of the expected signal and non-uniform noise. Subsequently, we employ the variational Bayesian inference (VBI) to learn the hyperparameters and use the first-order Taylor linear expansion to address the modeling error. The results of simulation experiments confirm that the proposed method demonstrates excellent performance compared to the existing algorithm.