Sparse Bayesian Learning-Based Direct Localization for Distributed Sensor Arrays with Unknown Gain and Phase Errors

Abstract

This paper presents a robust sparse direct position determination (DPD) method for multiple emitters using distributed sensor arrays in the presence of unknown gain-phase errors. The proposed method tackles the problem under a block sparse Bayesian learning (BSBL) framework, which incorporates perturbed steering vector factorization to separate the position parameter from the gain-phase errors, making dictionary completely known without learning. This paper devises a customized hyperparameter update rule for the proposed DPD model within the foundation of the BSBL-EM method, allowing for varying block parameters instead of constraining them to be consistent. The position estimates of emitters are determined by calculating the mean value of the posterior distribution of the reconstructed waveforms. Simulations demonstrate the superior performance of the developed BSBL direct localization method over its state-of-the-art rivals, which exhibits enhanced localization accuracy and robustness against gain-phase errors.