Circuits, Systems, and Signal Processing

, Volume 37, Issue 5, pp 1907–1934 | Cite as

A Super-Resolution Direction of Arrival Estimation Algorithm for Coprime Array via Sparse Bayesian Learning Inference

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Abstract

In this paper, we address the problem of direction of arrival (DOA) estimation with coprime array in the context of sparse signal reconstruction to fully exploit the enhanced degrees of freedom (DOF) offered by the difference coarray. The proposed method is based on the framework of sparse Bayesian learning and can jointly refine the unknown DOAs and the sparse signals in a gradual and interweaved manner. Specifically, the proposed approach is constructed by iteratively decreasing a surrogate function majorizing a given objective function, which results in accelerating the speed to converge to the global minimum. Furthermore, for facilitating a noise-free sparse representation, a customized linear transformation is judiciously incorporated in our sparsity-inducing DOA estimator to eliminate the unknown noise variance, and in the mean time, the sample covariance matrix perturbation can be normalized to an identity matrix as a by-product. Extensive simulation experiments under different conditions finally demonstrate the superiority of our suggested algorithm in terms of mean-squared DOA estimation error, DOF and resolution ability over state-of-the-art techniques.

Keywords

Direction of arrival (DOA) Coprime array sparse signal reconstruction Degrees of freedom (DOF) Sparse Bayesian learning (SBL) 

Notes

Acknowledgements

This research is supported partially by the National Natural Science Foundation of China (Nos. 11527809, 61571366), the National Key Research and Development Program of China (No. 2016YFC1400200) and the Fundamental Research Funds for the Central Universities of China (No. G2016KY0308).

Supplementary material

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.School of Marine Science and TechnologyNorthwestern Polytechnical UniversityXi’anChina
  2. 2.National Laboratory of Radar Signal ProcessingXidian UniversityXi’anChina

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