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.

In this letter, we propose a generalized nested array (GNA) with two flexible co-prime factors for enlarging the inter-element spacing of two concatenated uniform linear subarrays. It is shown that both the prototype nested array and generalized co-prime array can be interpreted as special cases. The closed-form expressions for the range of consecutive lags and the number of unique lags are derived for any given factors. After optimization, GNA has the same number of degrees of freedom as (super) nested array but with reduced mutual coupling. Numerical simulations prove the superiority of proposed configuration using compressed sensing algorithm.

In this paper, we propose a generalized co-prime multiple input multiple output (MIMO) configuration for direction of arrival (DOA) estimation. Compared with the conventional co-prime MIMO radar that requires prototype co-prime arrays (CPAs) for transmitter and receiver, the proposed configuration enlarges the inter-element spacing of transmitter with an integer expansion factor. By vectorizing the sample covariance matrix, the generalized sum and difference coarray (GSDC) concept is defined for exploiting the advantages of proposed configuration. The analytical expressions for the expansion factor, the maximum consecutive lags and the number of unique lags are derived carefully. It is verified that GSDC can obtain more degrees of freedom (DOFs) with the increase of expansion factor. Specifically, with O(M+N) sensors, GSDC can provide O(M2N2) DOFs, whereas the conventional one only has O(MN) DOFs. Simulation results demonstrate the usefulness of proposed configuration utilizing both spatial smoothing and sparse representation algorithms.

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.