Efficient angle and polarization parameter estimaiton for electromagnetic vector sensors multiple-input multiple-output radar by using sparse array

Qian-Peng Xie; Xiao-Yi Pan; Ji-Yuan Chen; Shun-Ping Xiao

doi:10.7498/aps.69.20191895

Journals >Acta Physica Sinica >Volume 69 >Issue 7 >Page 074302-1 > Article

Acta Physica Sinica
Vol. 69, Issue 7, 074302-1 (2020)

Efficient angle and polarization parameter estimaiton for electromagnetic vector sensors multiple-input multiple-output radar by using sparse array

Qian-Peng Xie, Xiao-Yi Pan^*, Ji-Yuan Chen, and Shun-Ping Xiao

Author Affiliations

State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Changsha 410073, China

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DOI: 10.7498/aps.69.20191895 Cite this Article

Qian-Peng Xie, Xiao-Yi Pan, Ji-Yuan Chen, Shun-Ping Xiao. Efficient angle and polarization parameter estimaiton for electromagnetic vector sensors multiple-input multiple-output radar by using sparse array[J]. Acta Physica Sinica, 2020, 69(7): 074302-1 Copy Citation Text

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Fig. 1. EMVS-MIMO radar system with sparse linear array.

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Fig. 2. Comparison of computational complexity versus different snapshots number.

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Fig. 3. Scatter plot of the angle parameters and polarization parameters by using the proposed method: (a) Scatter plot of the transmit elevation angle and receive elevation angle; (b) scatter plot of the transmit azimuth angle and receive azimuth angle; (c) scatter plot of the transmit elevation angle and azimuth angle; (d) scatter plot of the transmit polarization angle and polarization phase difference; (e) scatter plot of the receive elevation angle and azimuth angle; (f) scatter plot of the receive polarization angle and polarization phase difference.

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Fig. 4. The effect of the SNR for different methods: (a) Curves of RMSE versus SNR; (b) curves of PSD versus SNR.

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Fig. 5. The effect of the snapshot for different methods: (a) Curves of RMSE versus snapshot; (b) curves of PSD versus snapshot.

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Fig. 6. Comparison of target resolution ability of different methods: (a) curves of RMSE versus angular separation; (b) curves of PSD versus angular separation.

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算法类型	计算复杂度	计算时间/s
ESPRIT-Like算法 [19]	$\begin{aligned}& o((6 M)^2(6N)^2L + (6 M)^3(6 N)^3 + 2K^26(N + M - 2) + 6{K^3} \\ & + 7(M + N){K^2} + 12 K + 36 MN(36 MN - K) + (36 MN - K){K^2}) \end{aligned} $	28.332
PM-Like算法 [20]	$\begin{aligned}& o((6M)^2(6N)^2L + 72MN{K^2} + 2{K^2}6 (N + M - 2) + 6{K^3} \\& + 7( {M + N} ) {K^2} + 12K + 36MN({36MN - K}) + (36MN - K){K^2})\end{aligned} $	2.0698
Tensor子空间算法 [21]	$\begin{aligned} & o((6 M)^2(6N)^2L + 4(6M)^3(6N)^3 + 2K^2 6(N + M - 2) + 6{K^3} \\& + 7( {M + N} ) K^2 + 12 K + 36 MN( {36 MN - K} ) + (36 MN - K)K^2) \end{aligned} $	109.880
所提算法	$\begin{aligned}& o(\kappa ( 3K^3+ 108 MNKL + 3K^2) + \kappa (3K^2(36 MN + 6 NL + 6 ML)) \\ & + 2{K^2}6({N + M - 2} ) + 6{K^3} + 7(M + N){K^2} + 12K) \end{aligned} $	0.5684

Table 1.

Computational complexity comparison of different methods.

不同算法的计算复杂度对比

目标	方位角 θ/(°)	俯仰角 ϕ/(°)	极化角 γ/(°)	极化相位差 η/(°)
1	$40/24$	$15/21$	$10/42$	$38/17$
2	$20/38$	$25/32$	$22/33$	$48/27$
3	$30/16$	$35/55$	$45/60$	$56/39$

Table 2. Parameters of target signals.

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