基于互协方差稀疏重构的MIMO雷达低仰角估计算法

doi:10.12305/j.issn.1001-506X.2021.05.08

Abstract

Abstract:

For the estimation of the elevation angle of low-altitude targets, the aliasing among multipath signals seriously affects the angle measurement performance of the radar. The direction of arrival (DOA) estimation algorithm based on compressed sensing theory and the multi-input and multi-output (MIMO) radar system are combined to apply to the DOA estimation of low-altitude targets, and a low-altitude target DOA estimation algorithm for MIMO radar based on sparse reconstruction of cross-covariance matrix is proposed. Firstly, the vectorization of the virtual matrix after the generalized matched filtering of the multipath received signal for the MIMO radar is processed. In view of the disadvantages of large computation caused by vectorization of virtual aperture expansion, the dimension reduction processing is carried out to reduce the computation. Then, the advantage of noise independence and uncorrelation in the cross-covariance matrix of multiple snapshots is utilized to reduce the impact of noise and improve estimation performance of the algorithm. Finally, the problem is transformed into convex optimization problem for sparse recovery. Simulation results show that this algorithm can still effectively estimate the elevation angle of low-altitude targets even when the direct signal and the multipath reflected signal are weakened each other, which has better performance in estimating the elevation angle of low-altitude targets Compared with L1-SVD and L1-SRACV algorithms.

Key words: cross-covariance matrix, compressed sensing, multi-input and multi-output radar, direction of arrival (DOA) estimation

CLC Number:

TN953

Zixin ZHANG, Guoping HU, Hao ZHOU, Chenghong ZHAN. Low elevation angle estimation algorithm for MIMO radar based on sparse reconstruction of cross-covariance[J]. Systems Engineering and Electronics, 2021, 43(5): 1218-1223.

Figures/Tables 6

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Low elevation angle estimation algorithm for MIMO radar based on sparse reconstruction of cross-covariance

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