Estimation In 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.

PSWF-based decoupled atomic norm minimization for DOD and DOA estimation in MIMO radar with arbitrary linear arrays

Decoupled atomic norm minimization (D-ANM) is a computationally-efficient gridless two-dimensional parameter estimation method via dividing the two-level Toeplitz matrix into two one-level matrices, and can be applied to the one-snapshot direction-of-departure (DOD) and direction-of-arrival (DOA) estimation in bistatic multiple-input multiple-output (MIMO) radar with uniform linear arrays (ULAs). Nevertheless, in actual applications, the transmitting and receiving arrays are not always ULAs, which leads to the Toeplitz structure not satisfied and the regular D-ANM algorithm not available. In this work, the one-snapshot DOD and DOA estimation in bistatic MIMO radar with arbitrary linear arrays (ALAs) is investigated. The Prolate spheroidal wave functions (PSWFs) are utilized to express such a D-ANM problem followed by a semi-definite programming (SDP) solution. The PSWF-based method can extend the application of D-ANM from ULAs to ALAs, and avoid the grid partition of the conventional sparse representation methods. The numerical experiments are conducted to verify the proposed method.

Explicit bounds for joint range and direction-of-arrival estimation in MIMO radar

Bayesian bounds, such as the Ziv–Zakai bound (ZZB), can provide a tight baseline for the performance of estimators or an accurate prediction for the performance limit of radar systems under all signal-to-noise ratio (SNR) conditions. However, there exists very limited literature that deals with the explicit bound for joint estimation of multi-parameters in radar systems. In this paper, we employ the thought of Shannon’s information theory to propose a tight bound with closed-form expression for joint range and direction-of-arrival (DOA) estimation in multiple-input multiple-output radar. Based on the joint a posteriori probability density function, we define range-DOA entropy error (RDEE) as the entropy power of range and DOA. With normalized a posteriori entropies in low and high SNRs, the closed-form approximation for RDEE is derived. The explicit RDEE is comprehensive and captures the effect of the SNR, the number of array elements, and the ratio of the signal bandwidth to the carrier frequency. RDEE is compared with the ZZB by a numerical simulation and the result shows RDEE and ZZB are almost identical in low and high SNRs.

Array antenna failure robust compensation via tensor Hankelization in MIMO radar for DOA estimation

Utilizing low-rank tensor completion (LRTC) provides great success in the recovery of missing entries by exploiting the multidimensional structure. However, the assumption of most LRTC methods that the observed samples are randomly distributed is violated in multiple-input multiple-output (MIMO) radar under antenna failure, in which some fibers or slices are missing in the third-order measurement tensor. An antenna failure compensation method based on Hankel low-rank tensor recovery is proposed for robust direction-of-arrival (DOA) estimation in MIMO radar. To reduce computational burdens and noise sensitivity, we first use higher-order singular value decomposition (HOSVD) to lower the dimensions of the observations. Then, each frontal slice of the reduced-dimensional tensor is mapped into twofold Hankel structure to better reveal the relationship among different fibers. We further explore the Hankelization operation in a tensor manner to constitute high-dimensional structured Hankel tensor by concatenating the subtensors to capture the slice correlations. Because of the stronger low-rank property and the lack of entirely missing fibers or slices, the LRTC method can be applied to this structured Hankel tensor to recover structurally incomplete tensor. Simulation experiments verify that the proposed method achieves the robust compensation of faulty antennas in MIMO radar, ensuring remarkable DOA estimation performance.

Beamspace Scene Classification Algorithm for Low-Angle Estimation in MIMO Radar

This paper discusses the issue of low-angle estimation in multiple-input, multiple-output (MIMO) radar. Due to the low amount of data transmission, storage, and computation required for beamspace super-resolution algorithms in low-angle estimation, the method has gained considerable interest in recent years. This paper develops a beamspace scene classification (BSC) algorithm to enhance the performance of low-angle estimation in MIMO radar. The proposed BSC algorithm solves an initial angle estimate and the multipath coefficient estimate using the 3D beamspace data, and further constructs the beamspace data required for transmitting and receiving sides to reduce the data dimensions. It is additionally used to provide three estimation schemes with closed-form solutions for three multipath scenes (obtaining two estimations for transmitting and receiving sides). Finally, it fuses the estimates of the two sides by the minimum variance criterion. As a result, the proposed method achieves high estimation accuracy while requiring few processing resources. Moreover, the computational complexity of the proposed algorithm is examined in this study, with the results demonstrating that the proposed method is superior for engineering applications.

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.

Tensor Decompositions in Wireless Communications and MIMO Radar

The emergence of big data and the multidimensional nature of wireless communication signals present significant opportunities for exploiting the versatility of tensor decompositions in associated data analysis and signal processing. The uniqueness of tensor decompositions, unlike matrix-based methods, can be guaranteed under very mild and natural conditions. Harnessing the power of multilinear algebra through tensor analysis in wireless signal processing, channel modeling, and parametric channel estimation provides greater flexibility in the choice of constraints on data properties and permits extraction of more general latent data components than matrix-based methods. Tensor analysis has also found applications in Multiple-Input Multiple-Output (MIMO) radar because of its ability to exploit the inherent higher-dimensional signal structures therein. In this paper, we provide a broad overview of tensor analysis in wireless communications and MIMO radar. More specifically, we cover topics including basic tensor operations, common tensor decompositions via canonical polyadic and Tucker factorization models, wireless communications applications ranging from blind symbol recovery to channel parameter estimation, and transmit beamspace design and target parameter estimation in MIMO radar.

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.

New Approach for DOA Estimation in MIMO Radar With Nonorthogonal Waveforms

It is desirable to emit orthogonal waveforms in multiple-input multiple-output (MIMO) radar systems. Unfortunately, the waveform orthogonality may not be always guaranteed in practice. Nonorthogonal transmit waveforms will incur two challenges i.e., spatially colored noise and nonideal virtual direction matrix, for direction-of-arrival (DOA) estimation in colocated MIMO radar. In this article, a temporal cross-correlation matrix-based algorithm is proposed to overcome the above drawbacks. Also, the stochastic Cramer-Rao bound (CRB) is derived. The proposed algorithm does not require the prior knowledge of the waveform correlation matrix. Moreover, it is attractive from the perspective of efficiency as well as accuracy. Numerical experiments demonstrate the effectiveness of our algorithm.

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.

DOA Estimation in MIMO Radar With Non-Perfectly Orthogonal Waveforms

In direction of arrival (DOA) estimation of multiple input multiple output (MIMO) radar, many existing publications deal with perfectly orthogonal waveform signals (POWS). But in practice, it is a real challenge to generate POWS. In this letter, we study DOA estimation in MIMO radar with the case of non-perfectly orthogonal waveform signals (Non-POWS). A two-step algorithm is proposed. The first step is to blend the covariance matrix of Non-POWS into the steering matrix to set up a new signal model similar to the POWS model. The second step is to perform pre-whitening processing for the colored noise produced by Non-POWS utilizing known transmitted signal covariance matrix. Simulation results demonstrate that: 1) the Non-POWS degrades the DOA estimation performance and 2) our method can solve this problem well.

Noncircular Sources-Based Sparse Representation Algorithm for Direction of Arrival Estimation in MIMO Radar with Mutual Coupling

In this paper, a reweighted sparse representation algorithm based on noncircular sources is proposed, and the problem of the direction of arrival (DOA) estimation for multiple-input multiple-output (MIMO) radar with mutual coupling is addressed. Making full use of the special structure of banded symmetric Toeplitz mutual coupling matrices (MCM), the proposed algorithm firstly eliminates the effect of mutual coupling by linear transformation. Then, a reduced dimensional transformation is exploited to reduce the computational complexity of the proposed algorithm. Furthermore, by utilizing the noncircular feature of signals, the new extended received data matrix is formulated to enlarge the array aperture. Finally, based on the new received data, a reweighted matrix is constructed, and the proposed method further designs the joint reweighted sparse representation scheme to achieve the DOA estimation by solving the l 1 -norm constraint minimization problem. The proposed method enlarges the array aperture due to the application of signal noncircularity, and in the presence of mutual coupling, the proposed algorithm provides higher resolution and better angle estimation performance than ESPRIT-like, l 1 -SVD and l 1 -SRDML (sparse representation deterministic maximum likelihood) algorithms. Numerical experiment results verify the effectiveness and advantages of the proposed method.

Parameter estimation and identifiability in bistatic multiple-input multiple-output radar

An iterative ESPRIT-like algorithm is devised for direction-of-departure (DOD) and direction-of-arrival (DOA) estimation in multiple-input multiple-output radar. Our proposal can handle identical DODs and DOAs, and provides autopairing of the angle parameters. Furthermore, it is proved that the multiple signal classification methodology cannot identify (MN – 1) targets, where M and N are the element numbers in the transmit and receive antennas, respectively. Simulation results are included to evaluate the performance of the proposed algorithm.

Adaptive DOA Estimation in MIMO Radar based on Canonical Correlation Analysis Method

In multiple-input multiple-output (MIMO) radar, independent waveforms are transmitted from different antennas, and the target parameters are estimated via the linearly independent echoes frim different targets.Several adaptive approaches are directly applied to target angle and target amplitude estimation, including Capon, APES(amplitude and phase estimation). The CCA (canonical correlation analysis) approach is first proposed to estimate target locations which has high peak amplitudes, then a gradient-based algorithm is presented to improve the target angle estimation accuracy based on CCA approach without spectrum peak searching. With an initial angle, the angle sequence is iteratively updated with adaptive steps and converges to local peaks which indicate the target locations. Simulations show that the target angle accuracy is improved, and the common DOA (direction-of -arrive) problem is avoided.

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.

Improved joint angle and Doppler frequency estimation for MIMO radar

An improved method for joint angle and Doppler frequency estimation in bistatic multiple-input multiple-output (MIMO) radar is proposed. The characteristic of the Vandermonde matrix is employed to expand the virtual array in MIMO radar. Then the rotational invariance produced by the time delay sampling is extracted to estimate the angles and Doppler frequencies. The proposed algorithm is suitable for non-uniform linear arrays and the estimations of Doppler frequency, direction of departure (DOD) and direction of arrival (DOA) are automatically paired. The estimation performance of our approach is better than the estimation of signal parameters via rotational invariance techniques (ESPRIT)-based method due to the expansion of the virtual array, which also increase the maximum detection number of targets. Cramer–Rao bound of angle and Doppler frequency estimation in MIMO radar is derived. Simulation results verify the usefulness of our approach.

Sparse representation-based joint angle and Doppler frequency estimation for MIMO radar

An algorithm based on sparse representation for joint angle and Doppler frequency estimation in multiple-input multiple-output radar is proposed. Through the data reconstruction, the algorithm only...

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.

Joint DOD/DOA estimation in MIMO radar exploiting time-frequency signal representations

In this article, we consider the joint estimation of direction-of-departure (DOD) and direction-of-arrival (DOA) information of maneuvering targets in a bistatic multiple-input multiple-output (MIMO) radar system that exploits spatial time-frequency distribution (STFD). STFD has been found useful in solving various array processing problems, such as direction finding and blind source separation, where nonstationary signals with time-varying spectral characteristics are encountered. The STFD approach to array processing has been primarily limited to conventional problems for passive radar platform that deals with signal arrivals, while its use in a MIMO radar configuration has received much less attention. This paper examines the use of STFD in MIMO radar systems with application to direction finding of moving targets with nonstationary signatures. Within this framework, we consider the use of joint transmit and receive apertures for the improved estimation of both target time-varying Doppler signatures and joint DOD/DOA. It is demonstrated that the STFD is an effective tool in MIMO radar processing when moving targets produce Doppler signatures that are highly localized in the time-frequency domain.

Bayesian target location and velocity estimation for multiple-input multiple-output radar

For a non-coherent multiple-input multiple-output radar system, the minimum mean square error (MMSE) estimator and maximum a posteriori (MAP) estimator of the target location and velocity, considered random unknown parameters, are formulated and the corresponding posterior Cramer-Rao lower bound (PCRLB) is derived. Moreover, numerical solutions for the proposed MMSE estimator and the PCRLB are obtained by using Monte-Carlo methods because of the absence of closed-form solutions. The numerical results show that the mean square errors (MSEs) of the MMSE estimate and the MAP estimate converge to the corresponding PCRLB as the signal-to-noise ratio (SNR) increases when the number of transmit and receive antennas is sufficiently large. A linear approximation can be used to simplify the MMSE estimation. It is shown in some simulations that the linear approximation of the MMSE estimate is accurate at high SNR values and the SNR needed for accurate approximation can be reduced by increasing the number of antennas employed.