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A Novel ESPRIT-Based Algorithm for DOA Estimation with Distributed Subarray Antenna

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Abstract

Distributed subarray antennas (DSAs), which are sparse arrays consisting of two or more far separated subarrays, have many advantages over uniform linear array, especially for direction of arrival (DOA) estimation. However, they are subject to manifold ambiguity, which has significant influence on DOA estimation. In order to solve the manifold ambiguity of uncorrelated sources for DSA, a novel method based on estimation of signal parameters via rotational invariance technique by utilizing optimal subarray partition is proposed in this paper. In the proposed method, an optimized reference estimation is obtained by the rotational invariance between the new subarrays with optimal partition of DSA. The high accuracy and unambiguous DOA estimations are then disambiguated easily according to the optimized reference estimation. In this way, the performance of disambiguated DOA estimation can be enhanced in cases of the low signal-to-noise ratio and the large spacing between the subarrays. Moreover, the ambiguity threshold effect of DOA estimation for DSA is analyzed by means of the maximum a posteriori estimator. Computer simulation results show good performance of the proposed method and the effectiveness of the ambiguity threshold for DSA.

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Acknowledgments

The authors would like to sincerely thank all the anonymous reviewers, the Associate Editor, and the Editor-in-Chief for their valuable comments and suggestions, which have greatly improved the quality of this paper. This work is supported by National Natural Science Foundation of China( 61101244).

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Authors and Affiliations

  1. National Laboratory of Radar Signal Processing, Xidian University, Xi’an, Shaanxi, 710071, China

    Yan Ma, Baixiao Chen, Minglei Yang & Yi Wang

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  1. Yan Ma
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  2. Baixiao Chen
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  4. Yi Wang
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Correspondence to Yan Ma.

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Ma, Y., Chen, B., Yang, M. et al. A Novel ESPRIT-Based Algorithm for DOA Estimation with Distributed Subarray Antenna. Circuits Syst Signal Process 34, 2951–2972 (2015). https://doi.org/10.1007/s00034-015-9987-6

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