Low Complexity Two-Dimensional Direction Of Arrival Estimation Using Wideband Uniform Concentric Spherical Arrays

CHEN Haihua; WANG Peizhao

doi:10.11999/JEIT210142

Volume 44 Issue 2

Feb. 2022

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CHEN Haihua, WANG Peizhao. Low Complexity Two-Dimensional Direction Of Arrival Estimation Using Wideband Uniform Concentric Spherical Arrays[J]. Journal of Electronics & Information Technology, 2022, 44(2): 710-717. doi: 10.11999/JEIT210142

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CHEN Haihua, WANG Peizhao. Low Complexity Two-Dimensional Direction Of Arrival Estimation Using Wideband Uniform Concentric Spherical Arrays[J]. Journal of Electronics & Information Technology, 2022, 44(2): 710-717. doi: 10.11999/JEIT210142

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Low Complexity Two-Dimensional Direction Of Arrival Estimation Using Wideband Uniform Concentric Spherical Arrays

doi: 10.11999/JEIT210142

CHEN Haihua^{1, 2},
WANG Peizhao^{1
,
,}

1.
College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, China
2.
Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, Tianjin 300350, China

Received Date: 2021-02-18
Rev Recd Date: 2021-05-27

Available Online: 2021-06-21

Publish Date: 2022-02-25

Abstract

Abstract

A low complexity Two-Dimensional Direction Of Arrival (2D-DOA) estimation algorithm using wideband Uniform Concentric Spherical Arrays (UCSA) is proposed in this paper. The output signals of the wideband UCSA are firstly converted to phase mode signals, which are then compensated by filters to achieve Frequency Invariant (FI) characteristic. The FI characteristic of the array can reduce the computational complexity of the wideband signal processing. To reduce further the complexity of 2D-DOA estimation, a reduced-dimension MUltiple SIgnal Classification (MUSIC) algorithm using FI-UCSA is proposed. The equivalent steering vector of the phase mode signal is decomposed into two matrices, which are related to azimuth and elevation angles, respectively. The 2D MUSIC is then simplified to One-Dimensional (1D) search, which optimizes DOA estimation in reduced dimension and substantially lower the computation complexity. Simulation results show that the computational complexity of the proposed algorithm is greatly reduced compared with the 2D MUSIC algorithm. In addition, the estimation accuracy and the resolution are slightly improved.

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References(17)

References

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