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Low-Complexity DOA Estimation Based on Constraint Solution Space

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Abstract

The Weighted Subspace Fitting (WSF) algorithm is one of the universal algorithms in Direction-Of-Arrival (DOA) estimation, which is of high accuracy. However, it involves the multi-dimensional nonlinear optimization problem, and the computational complexity is usually high. In this paper, we propose a low-complexity DOA estimation algorithm based on constraint solution space. Firstly, we use ESPRIT algorithm to limit the solution space around the best solution and reduce the computational range. Then, we find the best solution in a smaller solution space constraint by Cramr-Rao Bound (CRB), and seek repeatedly until reaching the global optimal solution of WSF algorithm by using the space of the best solution. By limiting the searching process in smaller solution space, this strategy controls the direction of convergence and reduces computational complexity. The experimental results show that this algorithm needs less iterations when the same DOA accuracy is required, and the computational complexity is apparently reduced.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant Nos. 61972417 and 61872385; The Fundamental Research Funds for the Central University, with Nos. 18CX02134A, 18CX02137A and 19CX05003A-4.

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Authors and Affiliations

  1. College of Oceanography and Space Informatics, China University of Petroleum, Qingdao, 266580, China

    ShiBao Li, Li Sun, HaiHua Chen, JianHang Liu, TingPei Huang & DaYin Zhao

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  1. ShiBao Li
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  2. Li Sun
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  3. HaiHua Chen
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  4. JianHang Liu
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  5. TingPei Huang
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  6. DaYin Zhao
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Correspondence to ShiBao Li.

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Li, S., Sun, L., Chen, H. et al. Low-Complexity DOA Estimation Based on Constraint Solution Space. Wireless Pers Commun 111, 2435–2447 (2020). https://doi.org/10.1007/s11277-019-06994-8

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