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.

This paper addresses the problem of joint direction of departure (DOD) and direction of arrival (DOA) estimation with nested bistatic multiple input multiple output (MIMO) radar using tensor decomposition. We first employ the two-level nested transmit and receive arrays to develop the sum-difference coarray for constructing the Toeplitz and spatial smoothing matrices. We then generalize the three-way tensor model from DOD and DOA dimensions, and derive the optimized tensor by maximizing the number of detectable targets, where the existing COMFAC technique is exploited for angle estimation. We show that the proposed method can identify more targets and achieve better performance by enforcing the three-way structure information compared with the subspace-based algorithms. We also show that the conventional tensor model is just a special case. Finally, we derive the coarray Cramér-Rao Bound (CRB) for the nested MIMO radar, and also conduct a study for the conditions under which the CRB exists. Numerical simulations are provided to validate the theoretical analysis and demonstrate the performance improvement.

The electromagnetic vector sensor (EMVS) embedded multiple-input multiple-output (MIMO) radar, named EMVS-MIMO radar, offers a unique perspective on target localization, providing both two-dimensional (2D) angle and polarization status estimations of targets. However, the current frameworks limit the EMVS-MIMO radar's ability to achieve high-resolution 2D direction finding while maintaining low complexity. To address this limitation, this paper proposes a uniform rectangular array-based methodology with the inter-sensor spacing of the receiving array greater than half-wavelength, along with a high-resolution estimation strategy based on a parallel factor (PARAFAC) model. To optimize the identifiability of the PARAFAC model, the array measurement is first arranged into a third-order tensor, and the 6 possible PARAFAC tensor models are established. Factor matrices are then estimated via PARAFAC decomposition, and 2D direction finding is achieved by combining the spatial rotational invariance technique with the normalized vector-cross product technique. The proposed PARAFAC algorithm can be extended to other sparse uniform geometries, such as L-shaped arrays and parallel linear arrays. The larger array aperture and inherent tensor gain of the proposed PARAFAC approaches offer superior estimation performance compared to existing frameworks. Additionally, the flexibility of the PARAFAC model may make it suitable for single snapshot scenarios. Finally, the improvements of the proposed approaches are validated by numerical simulations.

The polarized massive multiple-input multiple-output (MIMO) technique has been regarded as a promising solution to millimeter wave (mmWave) communication systems, because it experiences more degrees-of-freedom than the scalar configuration, and it represents a significant opportunity for secure communication. To deliver smart service to terminals, it is essential to provide base stations (BS) with the capability of terminal’s direction-of-arrival (DOA) awareness. In this paper, a compressive sampling (CS) framework is proposed for two-dimensional (2D) DOA and polarization estimation in mmWave polarized massive MIMO systems. The proposed approach first reduces the data volume via a reduced-dimension matrix. Then it computes the signal subspace via the eigendecomposition of the compressed array measurement. Thereafter, the rotational invariance characteristic is utilized to form a normalized polarization steering vector. Finally, 2D-DOA and polarization are estimated by incorporating the Poynting vector and the least squares (LS) techniques. The proposed architecture is computationally much more economical than existing algorithms. Besides, it allows a mmWave BS to provide comparable estimation performance with arbitrary sensor geometry, which is more flexible than most of the existing architectures. Furthermore, it is robust to the sensor position error. Numerical simulations verify the advantages of the proposed framework.

The Angle-of-Arrival (AoA)-based approach is an appealing solution for unmanned aerial vehicle (UAV) positioning, and has received significant interest recently. In this article, we propose a novel framework for UAV three-dimensional (3-D) positioning, the core of which is to measure the two-dimensional (2-D) Angle-of-Departure (2D-AoD) and 2D-AoA via a bistatic multiple-input multiple-output (MIMO) radar. Unlike the existing positioning architectures, the MIMO radar is equipped with polarized array antennas. An estimator based on the parallel factor (PARAFAC) decomposition is developed. It first obtains the direction matrices via performing the PARAFAC decomposition of the array data. Thereafter, the rotational invariance characteristic is utilized to form a normalized polarization response vector, from which the 2D-AoD, 2D-AoA, and polarization status of the UAVs are achieved via incorporating the vector cross-product method and the least squares (LSs) technique. Finally, the 3-D positions of the UAVs are easily calculated via the location relationship between the 2D-AoD, 2D-AoA, and the coordinates of transmitting/receiving (Tx/Rx) array. The proposed framework is computationally friendly, and is capable of positioning anonymous UAV. Moreover, it is insensitive to the geometry of the Tx/Rx array, indicating that the proposed framework supports configurable Tx/Rx antennas. Simulation results are provided to verify our theoretical advantages.

Intelligent Reflecting Surface (IRS) offers new insight into Multiple-Input Multiple-Output (MIMO) radar systems, since it enables a MIMO radar to positioning targets from Non-Line-of-Sight (NLOS) directions. This paper investigates the Direction-of-departure (DOD) and Direction-of-Arrival (DOA) estimation in a bistatic MIMO radar, in which a backward IRS is exploited to receive the echoes reflected by the targets from NLOS viewpoint. A Reduced-Dimension Multiple Signal Classification (MUSIC) estimator is developed. Compared with the state-of-the-art MUSIC and iteratively approximation algorithm, the proposed method RD-MUSIC algorithm is computationally much more efficient. Theoretical analyses are given and numerical results corroborate our analysis.