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BSBL-Based DOA and Polarization Estimation with Linear Spatially Separated Polarization Sensitive Array

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Abstract

The problem of multiple incident signals’ direction of arrival (DOA) and polarization estimation with linear spatially separated polarization sensitive array (SS-PSA) is investigated in sparse Bayesian learning (SBL) framework. SS-PSA is widely studied due to its low mutual coupling compared with the spatially collocated polarization sensitive array. On the one hand, the sparse representation of data model of proposed array can be expressed skillfully as a block-sparse representation. On the other hand, block sparse Bayesian learning (BSBL) algorithm has excellent performance in recovering the block-sparse signals. Therefore, in this paper, BSBL algorithm is extended to SS-PSA for DOA and polarization estimation. Firstly, a sparse representation model without parameters coupling is established, where the sparse coefficient vector is a block-sparse signal. Secondly, the expectation–maximization method in BSBL framework, i.e., BSBL-EM algorithm, is proposed to recover the block-sparse signal. Lastly, angles estimation can be obtained from the recovered sparse signal according to the intra-block correlation. Simulation results demonstrate that the BSBL-EM algorithm used in DOA and polarization estimation with SS-PSA exhibits better performance compared with Block Orthogonal Matching Pursuit algorithm and Group Basis Pursuit.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant 61501504.

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Authors and Affiliations

  1. Air and Missile Defense College, Air Force Engineering University, Xi’an, 710051, China

    Binbin Li, Weixiong Bai, Guimei Zheng, Xingyu He & Bin Xue

  2. Unit 93557 of the PLA, Beijing, China

    Mingliang Zhang

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  1. Binbin Li
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  2. Weixiong Bai
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  3. Guimei Zheng
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  4. Xingyu He
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Correspondence to Guimei Zheng.

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Li, B., Bai, W., Zheng, G. et al. BSBL-Based DOA and Polarization Estimation with Linear Spatially Separated Polarization Sensitive Array. Wireless Pers Commun 109, 2051–2065 (2019). https://doi.org/10.1007/s11277-019-06667-6

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