Abstract
This paper considers the problem of robust angle of arrival (AOA) source localization in the presence of outliers by using sparsity regularization. Firstly, the adaptive regularization (AR) and group-based regularization (GR) are respectively developed based on the cluster information of intersections of pairwise bearing lines to handle outliers. However, the estimated source position based on pseudolinear equation is biased as there exists correlation between the measurement matrix and noise vector. To counter the bias problem, new AR-based instrumental-variable (ARIV) estimator and GR-based instrumental-variable (GRIV) estimator are finally developed by exploiting instrumental variables. Extensive simulations are performed to demonstrate the performance superiority of ARIV and GRIV over AR and GR as well as least-squares, l1-norm regularization estimator and Huber's M-estimates in localization accuracy and outlier selection.
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