Gridless DOA Estimation Algorithm for Planar Arrays with Arbitrary Geometry

CHEN Tao; SHI Lin; HUANG Guigen; WANG Xilin

doi:10.11999/JEIT210038

Volume 44 Issue 3

Mar. 2022

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(3): 1052-1058

CHEN Tao, SHI Lin, HUANG Guigen, WANG Xilin. Gridless DOA Estimation Algorithm for Planar Arrays with Arbitrary Geometry[J]. Journal of Electronics & Information Technology, 2022, 44(3): 1052-1058. doi: 10.11999/JEIT210038

Citation:

CHEN Tao, SHI Lin, HUANG Guigen, WANG Xilin. Gridless DOA Estimation Algorithm for Planar Arrays with Arbitrary Geometry[J]. Journal of Electronics & Information Technology, 2022, 44(3): 1052-1058. doi: 10.11999/JEIT210038

Citation:

PDF( 1374 KB)

Gridless DOA Estimation Algorithm for Planar Arrays with Arbitrary Geometry

doi: 10.11999/JEIT210038

1.
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
2.
Nanjing Research Institute of Electronics Technology, Nanjing 210000, China

Funds: The National Natural Science Foundation of China (62071137)

Received Date: 2021-01-11
Rev Recd Date: 2021-05-30

Available Online: 2021-08-26

Publish Date: 2022-03-28

Abstract

Abstract

Due to the good estimation performance in the case of off-grid, the gridless DOA estimation algorithms attract extensive attentions and researches in recent years, among which the most representative is the one based on Atomic Norm Minimization (ANM). With the development of Decoupled ANM (DANM) algorithm, the application of ANM to the field of two-dimensional DOA estimation is possible. However, the traditional DANM algorithm and its subsequent improved algorithms are only suitable for Uniform Rectangular Array (URA) or Sparse Rectangular Array (SRA), and is not suitable for planar arrays with arbitrary geometry. In order to solve the above problem, a gridless DOA estimation algorithm, B-DANM algorithm, is proposed for planar arrays with arbitrary geometry. B-DANM algorithm exploits the first Bessel function to expand the covariance data of the received signal of the actual planar antenna array, so as to obtain the DANM algorithm framework suitable for planar arrays with arbitrary geometry, and then the final DOA estimation result is obtained by solving the Semi-Definite Program (SDP) problem, Vandermonde decomposition of Toeplitz matrix, pairing of estimation parameters and angle transformation. The simulation results show that the B-DANM algorithm has the advantages of accuracy and resolution compared with the traditional two-dimensional DOA estimation algorithm in the direction finding system of planar arrays with arbitrary geometry.
- Gridless DOA estimation algorithm,
- Atomic Norm Minimization (ANM),
- Semi-definite program,
- The first Bessel function

FullText(HTML)

References(23)

References

[1]	张海, 陈小龙, 张涛, 等. 基于MUSIC算法的二次雷达应答信号分离方法[J]. 电子与信息学报, 2020, 42(12): 2984–2991. doi: 10.11999/JEIT190842 ZHANG Hai, CHEN Xiaolong, ZHANG Tao, et al. Overlapping secondary surveillance radar replies separation algorithm based on MUSIC[J]. Journal of Electronics &Information Technology, 2020, 42(12): 2984–2991. doi: 10.11999/JEIT190842
[2]	李建峰, 沈明威, 蒋德富. 互质阵中基于降维求根的波达角估计算法[J]. 电子与信息学报, 2018, 40(8): 1853–1859. doi: 10.11999/JEIT171087 LI Jianfeng, SHEN Mingwei, and JIANG Defu. Reduced- dimensional root finding based direction of arrival estimation for coprime array[J]. Journal of Electronics &Information Technology, 2018, 40(8): 1853–1859. doi: 10.11999/JEIT171087
[3]	SCHMIDT R O. Multiple emitter location and signal parameter estimation[J]. IEEE Transactions on Antennas and Propagation, 1986, 34(3): 276–280. doi: 10.1109/TAP.1986.1143830
[4]	ROY R and KAILATH T. ESPRIT-estimation of signal parameters via rotational invariance techniques[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1989, 37(7): 984–995. doi: 10.1109/29.32276
[5]	CANDES E J, ROMBERG J, and TAO T. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information[J]. IEEE Transactions on Information Theory, 2006, 52(2): 489–509. doi: 10.1109/TIT.2005.862083
[6]	DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289–1306. doi: 10.1109/TIT.2006.871582
[7]	CANDES E J and WAKIN M B. An introduction to compressive sampling[J]. IEEE Signal Processing Magazine, 2008, 25(2): 21–30. doi: 10.1109/MSP.2007.914731
[8]	BARANIUK R G. Compressive sensing[J]. IEEE Signal Processing Magazine, 2007, 24(4): 118–121. doi: 10.1109/MSP.2007.4286571
[9]	MALIOUTOV D, CETIN M, and WILLSKY A S. A sparse signal reconstruction perspective for source localization with sensor arrays[J]. IEEE Transactions on Signal Processing, 2005, 53(8): 3010–3022. doi: 10.1109/TSP.2005.850882
[10]	CHI Y, SCHARF L L, PEZESHKI A, et al. Sensitivity to basis mismatch in compressed sensing[J]. IEEE Transactions on Signal Processing, 2011, 59(5): 2182–2195. doi: 10.1109/TSP.2011.2112650
[11]	DAS A. Theoretical and experimental comparison of off-grid sparse Bayesian direction-of-arrival estimation algorithms[J]. IEEE Access, 2017, 5: 18075–18087. doi: 10.1109/ACCESS.2017.2747153
[12]	YANG Zai, XIE Lihua, and ZHANG Cishen. Off-grid direction of arrival estimation using sparse Bayesian inference[J]. IEEE Transactions on Signal Processing, 2013, 61(1): 38–43. doi: 10.1109/TSP.2012.2222378
[13]	CHANDRASEKARAN V, RECHT B, PARRILO P A, et al. The convex geometry of linear inverse problems[J]. Foundations of Computational Mathematics, 2012, 12(6): 805–849. doi: 10.1007/s10208-012-9135-7
[14]	CANDÈS E J and FERNANDEZ-GRANDA C. Towards a mathematical theory of super-resolution[J]. Communications on Pure and Applied Mathematics, 2014, 67(6): 906–956. doi: 10.1002/cpa.21455
[15]	TANG Gongguo, BHASKAR B N, SHAH P, et al. Compressed sensing off the grid[J]. IEEE Transactions on Information Theory, 2013, 59(11): 7465–7490. doi: 10.1109/TIT.2013.2277451
[16]	BHASKAR B N, TANG Gongguo, and RECHT B. Atomic norm denoising with applications to line spectral estimation[J]. IEEE Transactions on Signal Processing, 2013, 61(23): 5987–5999. doi: 10.1109/TSP.2013.2273443
[17]	YANG Yang, CHU Zhigang, XU Zhongming, et al. Two-dimensional grid-free compressive beamforming[J]. The Journal of the Acoustical Society of America, 2017, 142(2): 618–629. doi: 10.1121/1.4996460
[18]	YANG Yang, CHU Zhigang, and PING Guoli. Alternating direction method of multipliers for weighted atomic norm minimization in two-dimensional grid-free compressive beamforming[J]. The Journal of the Acoustical Society of America, 2018, 144(5): EL361–EL366. doi: 10.1121/1.5066345
[19]	YANG Yang, CHU Zhigang, and PING Guoli. Two-dimensional multiple-snapshot grid-free compressive beamforming[J]. Mechanical Systems and Signal Processing, 2019, 124: 524–540. doi: 10.1016/j.ymssp.2019.02.011
[20]	ZHANG Zhe, WANG Yue, and TIAN Zhi. Efficient two-dimensional line spectrum estimation based on decoupled atomic norm minimization[J]. Signal Processing, 2019, 163: 95–106. doi: 10.1016/j.sigpro.2019.04.024
[21]	TIAN Xiyan, LEI Jinhui, and DU Liufeng. A generalized 2-D DOA estimation method based on low-rank matrix reconstruction[J]. IEEE Access, 2018, 6: 17407–17414. doi: 10.1109/ACCESS.2018.2820165
[22]	LU Aihong, GUO Yan, LI Ning, et al. Efficient gridless 2-D direction-of-arrival estimation for coprime array based on decoupled atomic norm minimization[J]. IEEE Access, 2020, 8: 57786–57795. doi: 10.1109/ACCESS.2020.2982413
[23]	HUA Yingbo. A pencil-MUSIC algorithm for finding two-dimensional angles and polarizations using crossed dipoles[J]. IEEE Transactions on Antennas and Propagation, 1993, 41(3): 370–376. doi: 10.1109/8.233122