A Nyström-Based Low-Complexity Algorithm with Improved Effective Array Aperture for Coherent DOA Estimation in Monostatic MIMO Radar

Remote Sensing

卷:14

Teng Ma¹  Jiang Du¹  Huaizong Shao² 

[1] Department of Communication Engineering, Chengdu University of Information Technology, Chengdu 610225, China;

[2] Department of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China;

关键词: monostatic MIMO radar;    coherent targets;    angle estimation;    unitary transformation;    Nyström method;    aperture extension;   

DOI  :  10.3390/rs14112646

来源: DOAJ

【 摘 要 】

In this paper, we propose a computationally efficient algorithm with improved effective aperture for coherent angle estimation in a monostatic multiple-input multiple-output (MIMO) radar. First, the direction matrix of MIMO radar is mapped into a low-dimensional matrix of virtual uniform linear array (ULA). Then, an augmented data expansion matrix with improved effective aperture is obtained by exploiting the Vandermonde-like structure of the low-dimensional direction matrix and radar cross section (RCS) matrix to enlarge the aperture of the array. Next, a unitary transformation is used to transform the augmented matrix into a real value and the approximate signal subspace of the augmented matrix is obtained by the Nyström method, which can reduce the computational complexity. The eigenvectors of the approximate signal subspace are used to reconstruct the matrix for direct decorrelation processing. Finally, direction of arrivals (DOAs) can be estimated faster by utilizing the unitary ESPRIT algorithm since the rotation invariance of the extended reconstruction matrix still exists. The proposed algorithm has a lower total computational complexity, and the estimation accuracy is improved by utilizing real values and enlarging the array aperture for estimation. Several theoretical analyses and simulation results confirm the effectiveness and advantages of the proposed method.

【 授权许可】

Unknown