A Tensor-Based Subspace Approach for Bistatic MIMO Radar in Spatial Colored Noise

Xianpeng Wang,Junxiang Wang,Wei Wang,Xin Li

doi:10.3390/s140303897

Abstract

In this paper, a new tensor-based subspace approach is proposed to estimate the direction of departure (DOD) and the direction of arrival (DOA) for bistatic multiple-input multiple-output (MIMO) radar in the presence of spatial colored noise. Firstly, the received signals can be packed into a third-order measurement tensor by exploiting the inherent structure of the matched filter. Then, the measurement tensor can be divided into two sub-tensors, and a cross-covariance tensor is formulated to eliminate the spatial colored noise. Finally, the signal subspace is constructed by utilizing the higher-order singular value decomposition (HOSVD) of the cross-covariance tensor, and the DOD and DOA can be obtained through the estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm, which are paired automatically. Since the multidimensional inherent structure and the cross-covariance tensor technique are used, the proposed method provides better angle estimation performance than Chen's method, the ESPRIT algorithm and the multi-SVD method. Simulation results confirm the effectiveness and the advantage of the proposed method.

Highlights

Multiple-input multiple-output (MIMO) radar [1,2,3] has drawn increasing attention and has become a hot research topic in the area of radar
multiple-input multiple-output (MIMO) radar uses multiple antennas to emit simultaneously orthogonal waveforms instead of the coherent waveforms, which are used in the phased-array radar, and this waveform diversity endows MIMO radar with superior performance relative to phased-array radar
SVD of the cross-correlation matrix method is presented in [14], which is effective for MIMO radar with three or more transmit antennas to eliminate the influence of spatial colored noise

Summary

Introduction

Multiple-input multiple-output (MIMO) radar [1,2,3] has drawn increasing attention and has become a hot research topic in the area of radar. In [13], an ESPRIT-based method for bistatic MIMO radar DOD and DOA estimation is proposed, which can eliminate spatial colored noise. It is only effective for three transmit antennas configuration. By dividing the transmit array into two subarrays, a combined ESPRIT and SVD of the cross-correlation matrix method (denoted as Chen’s method) is presented in [14], which is effective for MIMO radar with three or more transmit antennas to eliminate the influence of spatial colored noise. A tensor-based frame is considered for the received signals, which exploits the multidimensional inherent structure and a novel tensor-based subspace for bistatic MIMO radar in the presence of spatial colored noise is proposed. Notation:Scalars, column vectors, matrices and tensor are expressed by regular, bold lowercase, bold uppercase and bold calligraphic letters, respectively. [A]i,j and [A]i,j,k stand for the (i, j) and (i, j, k) element of a matrix, A, and a tensor, A. (·)H , (·)T , (·)−1 and (·)∗ denote the Hermitian transpose, transpose, inverse and complex conjugation without transposition, respectively. ⊗ and ⊙ denote the Kronecker operator and the Khatri-Rao product, respectively. diag(·) denotes the diagonalization operation, and arg(γ) denotes the phase of γ

Tensor Basics

Bistatic MIMO Radar Signal Model

Tensor-Based Subspace Approach for Angle Estimation

Computational Complexity Analysis and Remark

Simulation Results

Conclusions