Off-grid DOA estimation under nonuniform noise via variational sparse Bayesian learning

• DOI: 10.1016/j.sigpro.2017.01.020
• Publication Date: 2017-02-04T14:47:27Z
• Authors: Chao Wen; Xuemei Xie; Guangming Shi
• Keywords: Robustness; Decorrelation; Direction of arrival

An off-grid DOA estimation method under nonuniform noise is proposed.The proposed method alleviates the nonuniformity of senor noise.A weighted partial virtual array output is exploited.A hierarchical Bayesian model is built with an almost Jeffreys prior incorporated.The proposed method can work without the knowledge of the number of sources. In this paper, the problem of direction-of-arrival (DOA) estimation in the presence of nonuniform noise is investigated, where the inherent off-grid effects in traditional sparsity-inducing algorithms are also considered. By formulating a sparse signal recovery problem for weighted partial virtual array (PVA) response, we develop a sparse Bayesian learning based method by exploiting joint sparsity between the power distribution of incident signals and the off-grid difference. In our proposed algorithm, a weighted partial covariance vector is obtained through the deliberate projection and decorrelation operations, which facilitates a sparse representation free from the nonuniform noise variances. Meanwhile, a variational Bayesian inference is implemented upon a hierarchical Bayesian learning model with an almost Jeffreys prior adopted, which strongly induces the sparsity and involves adaptively tuning sparseness-controlling parameters. Moreover, the proposed method works without the knowledge of the number of sources. Simulation results demonstrate it provides superiority in estimation precision and robustness against nonuniform noise.