Robust Angle Estimation for MIMO Radar with the Coexistence of Mutual Coupling and Colored Noise

Abstract

This paper deals with joint estimation of direction-of-departure (DOD) and direction-of- arrival (DOA) in bistatic multiple-input multiple-output (MIMO) radar with the coexistence of unknown mutual coupling and spatial colored noise by developing a novel robust covariance tensor-based angle estimation method. In the proposed method, a third-order tensor is firstly formulated for capturing the multidimensional nature of the received data. Then taking advantage of the temporal uncorrelated characteristic of colored noise and the banded complex symmetric Toeplitz structure of the mutual coupling matrices, a novel fourth-order covariance tensor is constructed for eliminating the influence of both spatial colored noise and mutual coupling. After a robust signal subspace estimation is obtained by using the higher-order singular value decomposition (HOSVD) technique, the rotational invariance technique is applied to achieve the DODs and DOAs. Compared with the existing HOSVD-based subspace methods, the proposed method can provide superior angle estimation performance and automatically jointly perform the DODs and DOAs. Results from numerical experiments are presented to verify the effectiveness of the proposed method.

Weighted Block Sparse Recovery Algorithm for High Resolution DOA Estimation with Unknown Mutual Coupling

“…In this way, the effect of mutual coupling between two closely located array sensors is considered. Based on the above analysis in ULAs, a banded symmetric Toeplitz matrix [10] is adopted to represent the mutual coupling matrix (MCM) in ULA. So D is a banded symmetric Toeplitz matrix, whose specific form is:…”

“…The above traditional DOA estimation algorithms are generally known as subspace-based algorithms. Because they are mainly based on eigenvalue decomposition (EVD) or singular value decomposition (SVD) of covariance matrix for DOA estimation, the performance is reduced in the case of low signal to noise ratio (SNR) or a limited number of snapshots [10][11][12][13]. In order to deal with the problems associated with traditional DOA estimation methods, the SSR algorithms, such as l 1 -SVD algorithm [14], sparse Bayesian learning (SBL) algorithm [15,16], l 1 -sparse representation of array covariance vector (SRACV) algorithm [17], and their derivative algorithms [18] were proposed in the past few years.…”

Joint Angle and Frequency Estimation Using One-Bit Measurements

“…In the past decades, various spectrum estimation algorithms have been proposed. Typical algorithms including multiple signal classification (MUSIC) [8][9][10], estimating signal parameters via rotational invariance technique (ESPRIT) [11,12], propagator method [13], maximum likelihood (ML) [14,15], tensor-based approaches [16][17][18][19][20], and optimization-aware algorithms [21][22][23][24][25][26][27]. Generally speaking, MUSIC is computationally inefficient as it requires multiple peak search.…”

Tensor-Based Angle Estimation Approach for Strictly Noncircular Sources with Unknown Mutual Coupling in Bistatic MIMO Radar

“…On the other hand, all algorithms mentioned above need to stack the received data into a special structure matrix, which ignores the inherence multidimensional structure of signal. To utilize the inherent multidimensional structure of the signals, many methods have been developed [ 21 , 22 , 23 , 24 , 25 ]. A multi-SVD algorithm is developed to estimate DOD and DOA in MIMO radar [ 21 ], and the estimation performance is improved remarkably.…”

“…Then, the estimation of DOD and DOA is obtained by using tensor-based subspace, and it can achieve better angle estimation accuracy with lower computational burden. In addition, the DODs and DOAs can be estimated in the coexistence of mutual coupling and spatial colored noise [ 25 ]. According to the above analysis, these algorithms only utilize the noncircularity and inherent multidimensional structure of strictly noncircular signals separately in the case of unknown mutual coupling.…”