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

Fangqing Wen | Zendy; Zijing Zhang | Zendy; Gong Zhang | Zendy; Yu Zhang | Zendy; Xinhai Wang | Zendy; Xinyu Zhang | Zendy

Open Access

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

Author(s) -

Fangqing Wen,

Zijing Zhang,

Gong Zhang,

Yu Zhang,

Xinhai Wang,

Xinyu Zhang

Publication year - 2017

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2017.2749404

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

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.

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