A Tensor-Based Covariance Differencing Method for Direction Estimation in Bistatic MIMO Radar With Unknown Spatial Colored Noise

• Main Authors: Fangqing Wen, Zijing Zhang, Gong Zhang, Yu Zhang, Xinhai Wang, Xinyu Zhang
• Format: Article
• Language: English
• Published: IEEE 2017-01-01
• Series: IEEE Access
• Subjects: Bistatic MIMO radar; direction estimation; spatial colored noise; covariance differencing; Tucker decomposition
• Online Access: https://ieeexplore.ieee.org/document/8026159/

In this paper, we investigate into direction estimation in bistatic multiple-input multiple-output (MIMO) radar in the presence of unknown spatial colored noise. Taking the stationary property of the spatial colored noise into consideration, a transform-based tensor covariance differencing method is proposed. The spatial colored noise is eliminated by forming the difference of the original and the transformed covariance matrices. To further exploit the inherent multidimensional nature, a fourth-order tensor is constructed, which helps to achieve more accurate subspace estimation. Thereafter, the traditional subspace-based methods are applied for ambiguous direction estimation. Finally, a special matrix is formed to associate the real angles with the targets. The proposed scheme does not bring virtual aperture loss, and it has complexity lower than the existing tensor-based subspace methods. Numerical simulations verify the improvement of our scheme.