Xianpeng Wang

Fast communication: Conjugate ESPRIT for DOA estimation in monostatic MIMO radar

In this paper, a novel conjugate ESPRIT (C-ESPRIT) method for direction of arrival (DOA) estimation in monostatic MIMO radar is proposed. Firstly, a reduced-dimensional matrix is employed to transform the data matrix into a low dimensional space. Then the properties of noncircular signals are utilized to construct a new virtual array, whose elements are twice that of the monostatic MIMO radar virtual array with distinct elements. The rotational invariance properties of the new virtual array are figured out to estimate DOA through ESPRIT. Compared with the reduced-dimensional ESPRIT (RD-ESPRIT), the proposed method improves the angular estimation accuracy significantly and detects more targets. Simulation results verify the effectiveness of the proposed method.

Tensor-based real-valued subspace approach for angle estimation in bistatic MIMO radar with unknown mutual coupling

In this paper, a tensor-based real-valued subspace approach for joint direction of departure (DOD) and direction of arrival (DOA) estimation in bistatic MIMO radar with unknown mutual coupling is proposed. Exploiting the inherent multidimensional structure of received data after matched filtering, a third-order measurement tensor signal model is formulated. For eliminating the effect of the unknown mutual coupling, a sub-tensor can be extracted from the third-order measurement tensor by taking advantage of the banded symmetric Toeplitz structure of the mutual coupling matrix (MCM). Then the sub-tensor can be turned into a real-valued one by forward-backward averaging and unitary transformation, and a real-valued signal subspace is constructed to estimate the DOD and DOA by the higher-order singular value decomposition (HOSVD). Owing to utilize the multidimensional structure of received data and forward-backward averaging technique, the proposed method has better angle estimation performance than MUSIC-Like and ESPRIT-Like algorithms. Furthermore, the proposed method is suitable for coherent targets. Simulation results verify the effectiveness and advantage of the proposed method. HighlightsWe formulate a tensor signal model for MIMO radar with unknown mutual coupling.We investigate tensor-based real-valued subspace approach for DOD and DOA estimation.The proposed method is suitable for coherent targets.The proposed method has better angle estimation performance than MUSI-Like and ESPRIT-Like algorithms.

Real-Valued Covariance Vector Sparsity-Inducing DOA Estimation for Monostatic MIMO Radar

In this paper, a real-valued covariance vector sparsity-inducing method for direction of arrival (DOA) estimation is proposed in monostatic multiple-input multiple-output (MIMO) radar. Exploiting the special configuration of monostatic MIMO radar, low-dimensional real-valued received data can be obtained by using the reduced-dimensional transformation and unitary transformation technique. Then, based on the Khatri–Rao product, a real-valued sparse representation framework of the covariance vector is formulated to estimate DOA. Compared to the existing sparsity-inducing DOA estimation methods, the proposed method provides better angle estimation performance and lower computational complexity. Simulation results verify the effectiveness and advantage of the proposed method.

DOA estimation for monostatic MIMO radar based on unitary root-MUSIC

Direction of arrival (DOA) estimation is an important issue for monostatic MIMO radar. A DOA estimation method for monostatic MIMO radar based on unitary root-MUSIC is presented in this article. In the presented method, a reduced-dimension matrix is first utilised to transform the high dimension of received signal data into low dimension one. Then, a low-dimension real-value covariance matrix is obtained by forward–backward (FB) averaging and unitary transformation. The DOA of targets can be achieved by unitary root-MUSIC. Due to the FB averaging of received signal data and the eigendecomposition of the real-valued matrix covariance, the proposed method owns better angle estimation performance and lower computational complexity. The simulation results of the proposed method are presented and the performances are investigated and discussed.

Fourth-order cumulants-based sparse representation approach for DOA estimation in MIMO radar with unknown mutual coupling

In this paper, a sparse representation approach based on fourth-order cumulants (FOC) is proposed for direction of arrival (DOA) estimation in monostatic multiple-input multiple-output (MIMO) radar with unknown mutual coupling. For applying the sparse representation theory successfully, exploiting the special banded symmetric Toeplitz structure of mutual coupling matrices (MCM) in both transmit array and receive array, the unknown MCM in received data can be turned into a diagonal one to eliminate the mutual coupling. Then based on the new received data, a reduced dimensional transformation matrix is formulated, and the proposed method further constructs a FOC matrix with special formation, which reduce the computational complexity of sparse signal reconstruction. Finally a reweighted l1-norm constraint minimization sparse representation framework is designed, and the DOAs can be obtained by finding the non-zero rows in the recovered matrix. Owing to utilizing the fourth-order cumulants and reweighted sparse representation framework, compared with ESPRIT-Like, FOC-MUSIC and l1-SVD algorithms, the proposed method performs well in both white and colored Gaussian noise conditions, meanwhile it has higher angular resolution and better angle estimation performance. Simulation results verify the effectiveness and advantages of the proposed method. HighlightsThe DOA estimation problem for monostatic MIMO radar with unknown mutual coupling is considered.Fourth-order cumulant matrix with advantageous formation is constructed to suppress colored Gaussian noises.A reweighted sparse representation framework of FOC matrix is proposed for the accurate DOA estimation.The proposed method provides better performance than ESPRIT-Like, l 1 - SVD and FOC-MUSIC algorithms in both white and colored Gaussian noise conditions.

Sparsity-Aware DOA Estimation Scheme for Noncircular Source in MIMO Radar

In this paper, a novel sparsity-aware direction of arrival (DOA) estimation scheme for a noncircular source is proposed in multiple-input multiple-output (MIMO) radar. In the proposed method, the reduced-dimensional transformation technique is adopted to eliminate the redundant elements. Then, exploiting the noncircularity of signals, a joint sparsity-aware scheme based on the reweighted l1 norm penalty is formulated for DOA estimation, in which the diagonal elements of the weight matrix are the coefficients of the noncircular MUSIC-like (NC MUSIC-like) spectrum. Compared to the existing l1 norm penalty-based methods, the proposed scheme provides higher angular resolution and better DOA estimation performance. Results from numerical experiments are used to show the effectiveness of our proposed method.

Covariance vector sparsity-aware DOA estimation for monostatic MIMO radar with unknown mutual coupling

In this paper, a covariance vector sparsity-aware DOA estimation method is proposed for monostatic multiple-input multiple-output (MIMO) radar with unknown mutual coupling. The new method firstly utilizes the banded symmetric Toeplitz structure of the mutual coupling matrix (MCM) in both of the transmit and receive arrays to eliminate the unknown mutual coupling. Then a sparse representation framework of the array covariance vector is formulated for obtaining the coarse DOA estimation. Finally, a refined maximum likelihood estimation procedure is introduced to estimate the DOA based on the recovered result. Compared with conventional algorithms, the proposed method provides higher angular resolution and better angle estimation performance. Furthermore, the computational complexity of the proposed method is reasonable, because it only involves single measurement vector (SMV) problem and does not require a dense discretized sampling grid for the recovered procedure. Simulation results are used to verify the effectiveness and advantages of the proposed method. HighlightsThe DOA estimation problem for monostatic MIMO radar with unknown mutual coupling is considered.A sparse representation framework of covariance vector is proposed for the coarse DOA estimation.A maximum likelihood estimation procedure is exploited for the accurate DOA estimation.The proposed method provides better performance than both l1-SVD and ESPRIT-Like algorithms.

A tensor-based subspace approach for bistatic MIMO radar in spatial colored noise.

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 Chens method, the ESPRIT algorithm and the multi-SVD method. Simulation results confirm the effectiveness and the advantage of the proposed method.

Propagator method for angle estimation of non-circular sources in bistatic MIMO radar

In this paper, the bistatic Multiple-Input Multiple-Output (MIMO) radar with non-circular signals is considered, and a propagator method for joint direction of departure (DOD) and direction of arrival (DOA) estimation is proposed. Owing to the properties of non-circular signals, a new virtual array whose elements are twice as many as MIMO virtual array is formed. In order to avoid the EVD (Eigenvalue Decomposition) or SVD (Singular Value Decomposition) of the covariance matrix, the propagator method is applied to estimate the signal subspace. Then the rotational invariance properties of the new virtual array are figured out, and the DODs and DOAs can be estimated through ESPRIT. The DODs and DOAs are paired automatically without the additional pairing computation. Compared with the ESPRIT and ESPRIT-MUSIC method in MIMO radar, the proposed method is able to provide better angle estimation performance and handle more targets. Some simulation results are used to verify the performance of the proposed method.

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

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.