•We investigate angle estimation for bistatic EMVS-MIMO radar.•A propagator method is derived for angle estimation.•The proposed algorithm is much efficient than the existing algorithms.•The proposed ...method brings more accurate angle estimation than the existing matrix-based methods.
In this Communication, we revisit the parameter estimation problem in a bistatic multiple-input multiple-output (MIMO) radar system with electromagnetic vector sensors (EMVS), and a modified algorithm is suggested. Firstly, the signal subspace is obtained via propagator method. Thereafter, automatically paired spatial angles are achieved by exploiting rotation invariant property as well as vector cross-product strategy. Finally, polarization parameters are calculated based on least squares technique. The suggested algorithm is analyzed in terms of identifiability, complexity, theoretical asymptotic error as well as Cramér-Rao bound (CRB). It is computationally-friendly and it offers automatically paired direction estimation. Moreover, it provides better estimation accuracy than state-of-the-art matrix-based methods. Simulation results verify the improvement of the proposed algorithm.
Multiple-input multiple-output (MIMO) is a technical hotspot in physical layer with numerous applications in wireless communications, radars, sonars, and well beyond. In this paper, we focus on the ...multi-dimensional angle estimation problem in a bistatic electromagnetic vector sensors (EMVS) MIMO system. Namely, we need to simultaneously estimate two-dimensional (2D) direction-of-arrival (DOA), 2D direction-of-departure (DOD), 2D receive polarization angle (RPA) and 2D transmit polarization angle (TPA). To tackle this issue, a parallel factor (PARAFAC) analysis-based estimator is proposed. Firstly, a third-order PARAFAC analysis data model is established, which can efficiently exploit the tensor structure of the array measurement. After performing PARAFAC decomposition on the tensor measurement, the factor matrices are achieved. By combining the estimation method of signal parameters via rotational invariance technique (ESPRIT) with the vector cross-product method, joint estimates of 2D-DOD, 2D-DOA, 2D-TPA and 2D-RPA are obtained without further pairing calculation. Compared with the state-of-the-art ESPRIT-Like approach, the proposed method can achieve better performance by enforcing the third-order structure information, and it is suitable for arbitrary array manifolds. Theoretical analyses are given and numerical results corroborate our analysis.
•We investigate angle estimation and mutual coupling self-calibration for ULA-based bistatic MIMO radar.•A PARAFAC-based two-step coarse/refined procedure is utilized for angle estimation.•The mutual ...coupling coefficients corresponding to the transmit array and the receive array are obtained via least square separately.•The proposed method brings no virtual aperture loss and achieves more accurate angle and mutual coupling estimation performance than the existing methods.
In this paper, we propose an effective scheme for angle estimation and array mutual coupling (MC) self-calibration in uniform linear arrays (ULA)-based bistatic multiple-input multiple-output (MIMO) radar. By exploiting the multidimensional inherent structure, the array data is formulated into a trilinear model. The transmit and receive direction matrices are primarily estimated via trilinear decomposition, after which the least square (LS) method is applied to obtain a rough angle estimation. Refined angles are achieved via two one-dimensional local searches. The MC coefficients are obtained via LS by utilizing the estimated angle prior. The proposed scheme can achieve automatic pairing of the estimated angles without any virtual aperture loss, thus it has better angle and MC estimation performance than existing methods. Numerical simulations verify the improvement of our scheme.
In this paper, we address the problem of angle estimation for bistatic multiple-input multiple-output (MIMO) radar in the presence of spatial colored noise. By exploiting the temporal structure and ...the multidimensional inherent structure, a fourth-order cross-covariance tensor is formulated to eliminate the effect of spatial colored noise. The higher-order singular value decomposition is utilized for accurate signal subspace estimation. Thereafter, angles are obtained with the shift-invariance technique. The proposed method can achieve automatic pairing of the estimated angles without any virtual aperture loss, thus it has better estimation performance than existing methods. Extensive numerical experiments verify the effectiveness and improvement of our algorithm.
•We investigate tensor-based subspace approach for angle estimation in a bistatic MIMO radar with unknown spatial colored noise.•A temporal cross-correlation tensor is constructed for de-noising.•The proposed method brings no virtual aperture loss and achieves more accurate angle estimation performance than the existing methods.
Direction-of-arrival (DOA) estimation is the preliminary stage of communication, localization, and sensing. Hence, it is a canonical task for next-generation wireless communications, namely beyond 5G ...(B5G) or 6G communication networks. Both massive multiple-input multiple-output (MIMO) and millimeter wave (mmW) bands are emerging technologies that can be implemented to increase the spectral efficiency of an area, and a number of expectations have been placed on them for future-generation wireless communications. Meanwhile, they also create new challenges for DOA estimation, for instance, through extremely large-scale array data, the coexistence of far-field and near-field sources, mutual coupling effects, and complicated spatial-temporal signal sampling. This article discusses various open issues related to DOA estimation for B5G/6G communication networks. Moreover, some insights on current advances, including arrays, models, sampling, and algorithms, are provided. Finally, directions for future work on the development of DOA estimation are addressed.
In this paper, a PARAFAC decomposition-based algorithm is developed for joint direction-of-departure and direction-of-arrival estimation in the presence of unknown mutual coupling for bistatic ...multiple-input multiple-output radar. A three-order tensor is formulated which links the estimations of coupled direction matrices to the PARAFAC model. The coupling effects of the direction matrices are compensated by two selective matrices, and the angles are obtained from the estimated direction matrices. Then the mutual coupling coefficients of the transmitter and the receiver are estimated using the subspace method. Unlike existing algorithms, PARAFAC decomposition before decoupling operation results in more accurate angle estimation, which brings better mutual coupling coefficients estimation than the ESPRIT-Like and unitary HOSVD methods. The proposed algorithm does not require spectral peak searching or eigenvalue decomposition of the received signal covariance matrix, and it can achieve automatic pairing of the estimated angles. The identifiability and computation complexity of the presented algorithm are analysed and Cramer–Rao bounds of joint angle and mutual coupling estimation are derived. Numerical experiments verify the effectiveness and improvement of our algorithm.
•A new method is developed for MIMO radar angle estimation with mutual coupling.•The proposed method brings no virtual aperture loss.•It achieves more accurate angle estimation performance than the existing methods.•The proposed method is more efficient with massive antennas.
Orthogonal waveform design is one of the key technologies that affects the detection performance of MIMO radars. Most of the existing methods indirectly tackle this problem as an intractable ...nonconvex optimization and an NP-hard problem. In this work, we propose a novel waveform design algorithm based on intelligent ions motion optimization (IMO) to directly obtain a set of polyphase codes with good orthogonality. The autocorrelation sidelobe and cross-correlation sidelobe are first derived and subsequently integrated into evaluation functions for evaluating the orthogonality of polyphase codes. In order to effectively cope with the aforementioned problem, we present a strengthened IMO that is highly robust and converges rapidly. In the liquid state, an optimal guiding principle of same-charge ions is suggested to enhance global search ability and avoid falling into local optima. An ion updating strategy based on fitness ranking is presented to improve the search efficiency in the crystal state. Finally, the improved algorithm is employed to optimize the polyphase codes. The experimental results, compared with other state-of-the-art algorithms, show that the polyphase codes obtained by the proposed algorithm have better orthogonality.
Orthogonal waveforms are often desirable to multiple-input multiple-output (MIMO) radar systems. Unfortunately, the orthogonality may not be always guaranteed in practice. In this letter, we consider ...the direction-of-arrival (DOA) estimation problem in colocated MIMO radar with imperfect waveforms, and a new methodology is presented. The noiseless cross-covariance matrix is obtained by utilizing the spatial cross-correlation technique. DOAs are obtained via reduced-dimension multiple signal classification (RD-MUSIC). In contrast to the state-of-the-art matrix completion (MC) algorithm, the proposed RD-MUSIC method is computationally more efficient and may have an estimation performance more accurate than the existing MC approach. The numerical results show the improvement in the proposed scheme.
Recent advance on signal processing has witnessed increasing interest in machine learning. In this paper, we revisit the problem of direction-of-arrival (DOA) estimation for colocated multiple-input ...multiple-output (MIMO) radar from the perspective of machine learning. The reduced-complexity transformation is first applied on the array data from matched filters, thus eliminating the redundancy of the array data for the relief of calculational burden. Furthermore, the pre-whitening is followed to obtain a simplified noise model. Finally, the DOA estimation is linked to off-grid sparse Bayesian learning (OGSBL), which does not require to update the noise hyper-parameter, and a block hyper-parameter is utilized to accelerate the convergence of the OGSBL algorithm. The proposed estimator provides better DOA estimation accuracy than the existing peak searching algorithm. The effectiveness of the proposed algorithm is verified via numerical simulation.
In this paper, we investigate the problem of joint direction-of-departure (DOD) and direction-of-arrival (DOA) estimation for bistatic multiple-input multiple-output radar with unknown spatially ...colored noise. By exploiting the sparse structure of the noise covariance matrix, a new de-noising scheme is designed. The signal covariance matrix is recast as a low-rank matrix with missing entries, which can be approximately tackled via solving an optimization problem. Thereafter, DODs and DOAs are obtained with the traditional subspace techniques. The proposed method does not bring any virtual aperture loss; thus, it achieves more accurate estimation performance than several state-of-the-art de-noising methods. Finally, the stochastic Cramer–Rao bound for joint direction finding is derived. Numerical computer simulations verify the effectiveness of the proposed algorithm.
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