Gridless DOA estimation method for monostatic MIMO array based on covariance matrix reconstruction

被引：0
|
作者：
Li X. [1 ]
Chen J. [1 ]
Tan W. [1 ]
Wen Y. [1 ]
Zhang R. [1 ]
机构：
[1] School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an
来源：
| 1600年 / Chinese Institute of Electronics卷 / 42期
关键词：
Direction-of-arrival (DOA) estimation; Gridless sparse representation; Joint sparsity signal recovery; Multiple-input multiple-output (MIMO); Noise-free covariance matrix reconstruction;
D O I：
10.3969/j.issn.1001-506X.2020.05.01
中图分类号：
学科分类号：
摘要：
A gridless direction-of-arrival (DOA) estimation method based on covariance matrix reconstruction in monostatic multiple-input multiple-output (MIMO) arrays is proposed. In this method, the MIMO arrays are equivalent to uniform linear arrays with enhanced signal-to-noise ratio (SNR) by the dimension reduction, and the problem of the target azimuth estimation is converted to sparse signal reconstruction based on mixed norm minimization (MixNM). Furthermore, a grid-based convex optimization problem equivalent is presented to the sparse reconstruction problem, which is modeled as a semi-definite programming problem. In order to solve the problem of the performance degradation caused by the grid size, the Toeplitz structure of the equivalent uniform linear array is used to model it into a semi-definite programming problem to reconstruct the noise-free covariance matrix. Finally, the target azimuth is estimated by Vandermonde decomposition. Compared with the traditional method based on the MixNM, the proposed method reduces the number of optimization variables. In addition, compared with other off-grid sparsity representation methods, the accuracy of the proposed method is not affected by the grid size, and it can estimate the coherent source targets. The simulation results show the effectiveness of the proposed method. © 2020, Editorial Office of Systems Engineering and Electronics. All right reserved.
引用
收藏
页码：969 / 977
页数：8
相关论文
共 20 条
  • [1] Xu G., Liu A., Jiang W., Et al., Joint user scheduling and antenna selection in distributed massive MIMO systems with limited back haul capacity, China Communications, 11, 5, pp. 17-30, (2014)
  • [2] Pailhas Y., Petillot Y., Brown K., Et al., Spatially distributed MIMO sonar systems: principles and capabilities, IEEE Journal of Oceanic Engineering, 42, 3, pp. 738-751, (2016)
  • [3] Pailhas Y., Petillot Y., Large MIMO sonar systems: a tool for underwater surveillance, Proc.of the Sensor Signal Processing for Defence (SSPD), pp. 1-5, (2014)
  • [4] Li J., Li D., Jiang D., Et al., Extended-aperture unitary root MUSIC-based DOA estimation for coprime array, IEEE Communications Letters, 22, 4, pp. 752-755, (2018)
  • [5] Liu A., Li F., Li B., Et al., Spatial polarimetric time-frequency distribution based DOA estimation: combining ESPRIT with MUSIC, EURASIP Journal on Wireless Communications and Networking, 1, (2018)
  • [6] Zhang X.F., Xu L.Y., Xu D., Et al., A low-complexity ESPRIT-based DOA estimation for colocated MIMO radar using reduced-dimension transformation, Electronic Letters, 47, 4, pp. 283-284, (2011)
  • [7] Zhang X., Xu L., Xu D., Direction of departure (DOD) and direction of arrival (DOA) estimation in MIMO radar with reduced-dimension MUSIC, IEEE Communications Letters, 14, 12, pp. 1161-1163, (2010)
  • [8] Chen P., Cao Z., Chen Z., Et al., Off-grid DOA estimation using sparse Bayesian learning in MIMO radar with unknown mutual coupling, IEEE Trans.on Signal Processing, 67, 1, pp. 208-220, (2018)
  • [9] Gerstoft P., Mecklenbrauker C.F., Xenaki A., Et al., Multi-snapshot sparse Bayesian learning for DOA, IEEE Signal Processing Letters, 23, 10, pp. 1469-1473, (2016)
  • [10] Shi Z., Zhou C., Gu Y., Et al., Source estimation using coprime array: a sparse reconstruction perspective, IEEE Sensors Journal, 17, 3, pp. 755-765, (2016)
12