Angle Estimation For Multiple-input Multiple-output Radar Research Articles

Enhanced angle estimation in MIMO radar: Combine RD-MUSIC and SDP optimization

Multiple Input Multiple Output (MIMO) radars are increasingly employed. However, with a growing array of antenna components, the dimension of the sampling covariance matrix and the computational complexity of traditional Direction of Arrival (DOA) and Direction of Departure (DOD) estimation algorithms increase exponentially. In order to accurately and effectively identify and locate signal sources and to optimize the computational complexity, this paper presents an innovative approach for detecting target numbers based on a Semi-Definite Programming (SDP) problem, which generates efficient solutions even for higher dimensions. In addition, the SDP’s robustness is developed through shrinkage adaptation using Large Covariance Matrices (LCMs), which are crucial for angle estimation. Following the effective detection of a target, the tapering estimation is applied to the LCMs under the computationally efficient Reduced-Dimension Multiple Signal Classification (RD-MUSIC) algorithm, which is more accurate and less computationally intensive than 2D search algorithms. Simulation outcomes demonstrate that the efficacy of our proposed model achieves high success rates in target detection and enhanced precision in DOA and DOD estimations.

A reduced-dimension RML method for transmit beamspace-based MIMO radar

In this paper, the problem of low angle estimation for multiple-input multiple-output (MIMO) radar is considered. Though the refined maximum likelihood (RML) algorithm can be directly applied to low angle estimation in MIMO radar, its computational burden is huge due to the large virtual aperture. Moreover, the conventional MIMO radar employs full waveform diversity, which actually sacrifices the emission gain. To reduce the computational burden and improve the transmit gain, a reduced-dimension RML method for transmit beamspace-based (TB-based) MIMO radar is developed. Firstly, the transmit beamspace (TB) matrix is designed to focus the transmit energy within the desired spatial sector. Then, to reduce the computational complexity and retain the estimation precision of the direct RML (D-RML) method for TB-based MIMO radar, a transmit-receive separate RML (TRS-RML) method is developed by applying the RML algorithm to the transmit and receive side respectively. Moreover, we propose to fuse the estimation results of the transmit and receive side together to improve the estimation precision. Theoretical analysis shows the TRS-RML method requires much lower computational burden than D-RML method. Numerical experiments show the proposed method has higher estimation accuracy compared with the D-RML method for the conventional MIMO radar in most angle region.

Cramér-Rao Bounds on Angle Estimating in Bistatic MIMO Radar With Non-Orthogonal Waveforms

Angle estimation is a hot topic in multiple input multiple output (MIMO) radar. However, most of the existing algorithms are only focus on the scenario of ideal orthogonal transmission waveform. In engineering applications, however, non-orthogonal waveforms scenario is more practical. In this paper, we focus on the Cramer-rao bound (CRB) on angle estimation in MIMO radar with non-orthogonal waveforms. Firstly, the angle estimation model in bistatic MIMO radar with non-orthogonal transmit waveforms is given, and the general expression of CRB is deduced. Thereafter, the CRBs in some special cases are discussed, e.g., monostatic geometry, orthogonal transmit waveforms scenario. In addition, theoretical comparisons between the stochastic CRBs and the deterministic ones are presented. Finally, the computer simulations are carried out to verify the theoretically ones.

Low angle estimation for MIMO radar with arbitrary array structures

ABSTRACTLow angle target estimation is always a difficult problem for radar elevation estimation. Multiple-input multiple-output (MIMO) radar can increase array degree of freedom and resolution, and improve the low angle target measurement precision. At the same time, the computation complexity of the MIMO radar is also increasing, especially for non-uniform linear array (ULA) configuration in which some fast algorithms cannot be used. In this paper, a fast low angle target estimation method is proposed for monostatic MIMO radar with arbitrary array configurations. With the array interpolation and the quasi-spatial-smoothing technique, the MIMO steering vector can be transformed into a virtual low dimension (typical similar size as the number of array elements) ULA steering vector. Then, the polynomial rooting algorithm is used to estimate the elevation of the low angle target. The simulation results of the proposed algorithm are presented and the performances are investigated and analysed.

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.

A low-complexity joint 2D-DOD and 2D-DOA estimation algorithm for MIMO radar with arbitrary arrays

In this article, we study the problem of four-dimensional angles estimation for bistatic multiple-input multiple-output (MIMO) radar with arbitrary arrays, and propose a joint two-dimensional direction of departure (2D-DOD) and two-dimensional direction of arrival (2D-DOA) estimation algorithm. Our algorithm is to extend the propagator method (PM) for angle estimation in MIMO radar. The proposed algorithm does not require peak searching and eigenvalue decomposition of received signal covariance matrix, because of this, it has low computational complexity. And it can achieve automatic pairing of four-dimensional angles. Furthermore, the proposed algorithm has much better angle estimation performance than interpolated estimation method of signal parameters via rotational invariance techniques (ESPRIT), and has very close angle estimation performance to ESPRIT-like algorithm which has higher computational cost than the proposed algorithm. We also analyze the complexity and angle estimation error of the algorithm, and derive the Cramer‐Rao bound (CRB). The simulation results verify the effectiveness and improvement of the proposed algorithm.

Compressive Sensing-Based Angle Estimation for MIMO Radar with Multiple Snapshots

The issue of angle estimation for multiple-input multiple-output (MIMO) radar is studied and an algorithm for the estimation based on compressive sensing with multiple snapshots is proposed. The dimension of received signal is reduced to make the computation burden lower, and then the noise sensitivity is reduced by the eigenvalue decomposition (EVD) of the covariance matrix of the reduced-dimensional signal. Finally the signal subspace obtained from the eigenvectors is realigned to apply the orthogonal matching pursuit (OMP) for angle estimation. The angle estimation performance of the proposed algorithm is better than that of estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm, and reduced-dimension Capon. Furthermore, the proposed algorithm works well for coherent targets, and requires no knowledge of the noise. The complexity analysis and simulation results verify the effectiveness of the algorithm.

Compressed Sensing Trilinear Model-Based Angle Estimation for Bistatic MIMO Radar

In this paper, we address the transmit angle and receive angle estimation problem for a bistatic multiple-input multiple-output (MIMO) radar. This paper links MIMO radar angle estimation problem to the compressed sensing trilinear model. Exploiting this link, it derives a compressed sensing trilinear model-based angle estimation algorithm, which can obtain automatically paired two-dimensional angle estimation. The proposed algorithm requires no spectral peak searching or pair matching, and it has better angle estimation performance than conventional algorithms including estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm. Simulation results illustrate performance of the algorithm.