Monostatic Monostatic Multiple-input Multiple-output Radar Research Articles

A Nyström-Based Low-Complexity Algorithm with Improved Effective Array Aperture for Coherent DOA Estimation in Monostatic MIMO Radar

In this paper, we propose a computationally efficient algorithm with improved effective aperture for coherent angle estimation in a monostatic multiple-input multiple-output (MIMO) radar. First, the direction matrix of MIMO radar is mapped into a low-dimensional matrix of virtual uniform linear array (ULA). Then, an augmented data expansion matrix with improved effective aperture is obtained by exploiting the Vandermonde-like structure of the low-dimensional direction matrix and radar cross section (RCS) matrix to enlarge the aperture of the array. Next, a unitary transformation is used to transform the augmented matrix into a real value and the approximate signal subspace of the augmented matrix is obtained by the Nyström method, which can reduce the computational complexity. The eigenvectors of the approximate signal subspace are used to reconstruct the matrix for direct decorrelation processing. Finally, direction of arrivals (DOAs) can be estimated faster by utilizing the unitary ESPRIT algorithm since the rotation invariance of the extended reconstruction matrix still exists. The proposed algorithm has a lower total computational complexity, and the estimation accuracy is improved by utilizing real values and enlarging the array aperture for estimation. Several theoretical analyses and simulation results confirm the effectiveness and advantages of the proposed method.

A novel reduced-dimensional beamspace unitary ESPRIT algorithm for monostatic MIMO radar

In this paper, a novel direction of arrival (DOA) estimation algorithm is proposed for monostatic multiple-input multiple-output (MIMO) radar. Due to redundancy of the received data of monostatic MIMO radar, the computational complexity of the inversion of the covariance matrix is increased. In order to reduce the large computational complexity of existing subspace-based algorithms, we perform two reduced-dimensional operations. First, the high-dimensional received data is transformed the low-dimensional received data through a special transformation matrix. Second, the low-dimensional received data is transformed into beamspace. The receive beamspace filter is designed using convex optimization, and it can flexibly control the bandwidth and limit the sidelobe level. To ensure that the converted noise is Gaussian white noise, the prewhitening process is performed. Based on the above precondition, the reduced-dimensional beamspace technique can be effectively combined with the unitary ESPRIT model. Finally, the Cramer-Rao bound (CRB) on angle estimation in element space and reduced-dimensional beamspace is calculated for performance analysis. Numerical simulations verify the effectiveness of the proposed algorithm.

Iterative reweighted proximal projection based DOA estimation algorithm for monostatic MIMO radar

The problem of nonconvex and nonsmooth sparse representation for direction of arrival (DOA) estimation in monostatic multiple-input multiple-output (MIMO) radar is addressed in this paper, which is dealt with by a novel iterative reweighted proximal projection method. The proposed method firstly obtains the array covariance vector by performing the vectorization operation on the reduced dimensional covariance matrix. Then a sparse representation framework is formulated for DOA estimation through minimizing the nonconvex and nonsmooth sparsity promoting function, and the weighted matrix, which is based on the high-order power of the inverse of the reduced dimensional covariance matrix, is designed for reweighting the nonconvex and nonsmooth minimization to enhance the sparsity of the solution. Thereafter, an iterative algorithm using proximal projection approach along with reweighted penalty ideas is developed to recover the sparse solution. Finally, DOA estimation is accomplished by searching the spectrum of the solution. Due to achieving a better approximation to the l0 norm, the proposed method exhibits better DOA estimation accuracy than the reweighted l1-SVD algorithm and reweighted SL0 algorithm. Furthermore, the proposed method can avoid any a-priori information on the number of targets. Simulation results are presented to verify the effectiveness of the proposed method.

Clutter-based gain and phase calibration for monostatic MIMO radar with partly calibrated array

Abstract The gain and phase calibration of monostatic multiple-input multiple-output (MIMO) radars with partly calibrated arrays is addressed and a clutter-based orthogonal projection (OP) method is introduced. When the transmitter and receiver of a monostatic MIMO radar are both uniform linear arrays with the same array spacing, a non-zero OP matrix for the joint transmit-receive array steering vectors can be found. The clutter output power projected on the OP matrix is zero if array is perfectly calibrated. Hence, the transmitter gain and phase can be calibrated by minimizing clutter projection output power for a well-calibrated receiver subarray. Subsequently, the unknown receiver gain and phase errors can be estimated with a similar projection method. Numerical simulation results confirm the effectiveness of the proposed method.

AOA, Delay, and Complex Propagation Factor Estimation for the Monostatic MIMO Radar System

In this paper, we propose a solution to find the angle of arrival (AOA), delay, and the complex propagation factor for the monostatic multiple-input multiple-output (MIMO) radar system. In contrast to conventional iterative computationally demanding estimation schemes, we propose a closed form solution for most of the previous parameters. The solution is based on forming an approximate correlation matrix of the received signals at the MIMO radar receiver end. Then, an eigenvalue decomposition (EVD) is performed on the formed approximate correlation matrix. The AOAs of the received signals are deduced from the corresponding eigenvectors. Then, the delays are estimated from the received signal matrix properties. This is followed by forming structured matrices which will be used to find the complex propagation factors. These estimates can be used as initializations for other MIMO radar methods, such as the maximum likelihood algorithm. Simulation results show significantly low root mean square error (RMSE) for AOAs and complex propagation factors. On the other hand, our proposed method achieves zero RMSE in estimating the delays for relatively low signal-to-noise ratios (SNRs).

Low complexity MUSIC‐based direction‐of‐arrival algorithm for monostatic MIMO radar

A low complexity multiple signal classification (MUSIC)-based angle estimation method in monostatic multiple-input multiple-output (MIMO) radar has been proposed. To reduce the computational burden and maintain the accuracy of solution, the beam-space reduced-complexity MUISC algorithm is developed for monostatic MIMO radar. As the results, the lower computational cost and higher angle resolution in pseudo spectral domain have been obtained. Some simulation results have been given to show the effectiveness of the proposed method.

Computationally efficient DOA estimation for monostatic MIMO radar based on covariance matrix reconstruction

A computationally efficient direction-of-arrival (DOA) estimation algorithm for monostatic multiple-input multiple-output (MIMO) radar based on covariance matrix reconstruction is presented. By reduced-dimension transformation, the transformed covariance matrix is reconstructed in the Toeplitz structure and then the DOAs are estimated by employing MUSIC. The proposed reduced-complexity denosing covariance matrix reconstruction approach provides lower computational complexity compared with conventional two-dimension multiple signal classification (2D-MUSIC). Moreover, in contrast to the subspace-based methods, the proposed method can be carried out without knowing the number of targets. Simulation results demonstrate the outperformance of the proposed method.

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.

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.

Multi-way Compressive Sensing Based 2D DOA Estimation Algorithm for Monostatic MIMO Radar with Arbitrary Arrays

This paper addresses the problem of two-dimensional (2D) direction of arrival (DOA) estimation for monostatic multiple-input multiple-output (MIMO) radar equipped with arbitrary arrays manifold. A low-complexity multi-way compressive sensing (MCS) 2D DOA estimation algorithm is derived in this paper. To ease the computational burden, the received data are first arranged into a trilinear model, and then three random compression matrixes are individually applied to reduce each tensor to a much smaller tensor. Finally, the 2D DOA estimation problem then links to the low-dimensional trilinear model. The proposed algorithm has estimation performance close to that of the trilinear decomposition algorithm while it is more appealing form the perspective of storage capacity. Compared with other estimation algorithms, such as multiple signal classification, our MCS algorithm requires neither eigenvalue decomposition of the received signal covariance matrix nor spectral peak searching. It also doesn't require array interpolation of the array geometry, therefore the proposed algorithm has much less computational load, which indicates the proposed algorithm will have wide application in the future for Massive MIMO systems.

Unitary reduced‐dimensional estimation of signal parameters via rotational invariance techniques for angle estimation in monostatic multiple‐input–multiple‐output radar with rectangular arrays

In this study, the issue of two-dimensional direction of arrival estimation in monostatic multiple-input–multiple-output (MIMO) radar with rectangular arrays is studied and an algorithm based on estimation of signal parameters via rotational invariance techniques (ESPRIT) is proposed. Through the reduced-dimensional transformation and unitary transformation, the proposed algorithm has very low complexity because of the low-dimension and real-valued computations. Meanwhile, the angle estimation performance of the proposed algorithm is improved compared with conventional ESPRIT and Unitary ESPRIT. The real-valued rotational invariance is utilised to achieve automatically paired estimations of the azimuth and elevation angles in monostatic MIMO radar. Error analysis of the angle estimation and Cramer-Rao bound in MIMO radar are derived. Simulation results verify the improvement and usefulness of our algorithm.

Computationally Efficient DOA Tracking Algorithm in Monostatic MIMO Radar with Automatic Association

We consider the problem of tracking the direction of arrivals (DOA) of multiple moving targets in monostatic multiple-input multiple-output (MIMO) radar. A low-complexity DOA tracking algorithm in monostatic MIMO radar is proposed. The proposed algorithm obtains DOA estimation via the difference between previous and current covariance matrix of the reduced-dimension transformation signal, and it reduces the computational complexity and realizes automatic association in DOA tracking. Error analysis and Cramér-Rao lower bound (CRLB) of DOA tracking are derived in the paper. The proposed algorithm not only can be regarded as an extension of array-signal-processing DOA tracking algorithm in (Zhang et al. (2008)), but also is an improved version of the DOA tracking algorithm in (Zhang et al. (2008)). Furthermore, the proposed algorithm has better DOA tracking performance than the DOA tracking algorithm in (Zhang et al. (2008)). The simulation results demonstrate effectiveness of the proposed algorithm. Our work provides the technical support for the practical application of MIMO radar.

Reduced-Dimensional ESPRIT for Direction Finding in Monostatic MIMO Radar with Double Parallel Uniform Linear Arrays

In this paper, the issue of two-dimensional direction of arrival estimation in monostatic multiple-input–multiple-output (MIMO) radar with double parallel uniform linear arrays is studied, and an algorithm based on estimation of signal parameters via rotational invariance techniques (ESPRIT) is proposed. Through a series of reduced-dimensional transformations, the proposed algorithm has very low complexity due to the low dimension. Meanwhile, the estimation performance of the proposed algorithm is slightly improved compared to the conventional ESPRIT, especially in low signal-to-noise ratio. Furthermore, the algorithm can estimate azimuth and elevation angles without additional pair matching in monostatic MIMO radar. Error analysis of the angle estimation and Cramer–Rao bound are derived. Simulation results verify the usefulness of our algorithm.

Reduced-complexity Capon for direction of arrival estimation in a monostatic multiple-input multiple-output radar

In this study, the authors consider the direction of arrival (DOA) estimation problem for a monostatic multiple-input multiple-output (MIMO) radar. A DOA estimation algorithm for the monostatic MIMO radar using Capon and the reduced-dimension transformation is proposed. The proposed algorithm can achieve lower computational complexity than the Capon algorithm, while not debasing the performance of the angle estimation. The proposed algorithm has better angle estimation performance than the Capon algorithm and estimation of signal parameters via rotational invariance techniques algorithm, and it has close angle estimation performance to the Cramér–Rao bound (CRB). The variance of the estimation error and the CRB of the DOA estimation are derived. Simulation results present the usefulness of the proposed algorithm.

Direction synthesis in DOA estimation for monostatic multiple input multiple output (MIMO) radar based on synthetic impulse and aperture radar (SIAR) and its performance analysis

A new direction synthetic method for monostatic multiple input multiple output (MIMO) radar is presented based on synthetic impulse and aperture radar (SIAR) system. Concerned with the monostatic MIMO radar which simultaneously emits orthogonal signals with multi-carrier-frequency and possesses sparsely distributed transmitting and receiving arrays with respective location, as well as the situation for the presence of multipath propagation in the low flying target’s echo, the method integrates the aperture of the transmitting arrays with the receiving arrays to form the digital beam-forming (DBF) in azimuth and elevation dimensions. And a study has been made of planar general MUSIC algorithm based on decorrelating the multipath signals of multi-carrier-frequency MIMO radar. Through compensating the phase delay of both the transmitting and the receiving arrays and synthesizing the transmitting beam in two dimensions at the receiver, the angular resolution and measurement accuracy are improved and the computational complexity is reduced after transforming the three-dimensional (3D) parameter estimation problem into a two-dimensional (2D) one. Finally, the Cramer-Rao Bounds (CRBs) of DOA estimation for azimuth and elevation is put forward with the exsiting multipath propagation. Results of computer simulation demonstrate the validity of the new method.