Monostatic Multiple-input Multiple-output Research Articles

Polynomial rooting-based parameter estimation for polarimetric monostatic MIMO radar

Compared with conventional multiple-input multiple-output (MIMO) radar, polarimetric MIMO radar has been proven to hold the additional capability to operate data in the joint spatial and polarization domains. However, practical transceivers may involve antenna arrays with classical non-ideal factors (e.g., gain/phase/position errors) and polarimetric non-ideal factors (e.g., polarimetric antenna misorientation), and many current approaches cannot handle such non-ideal factors in real applications. In this study, polynomial rooting-based direction-of-arrival (DOA) and polarization parameter estimation is investigated for polarimetric monostatic MIMO radar with non-ideal transceivers. By incorporating the polarimetric manifold separation (PMS) technique, a joint DOA and polarization parameter estimation method, termed PMS2, is proposed with a closed-form solution. The Cramér-Rao bound for the multi-dimensional parameters involved in the estimation problem is also given. Simulation results demonstrate the performance advantages of the proposed PMS2 approach, which is attributed to the compact structure of polarimetric transceivers and the ability of the PMS2 approach to handle the polarimetric properties of signals.

A Bandwidth Efficient Dual-Function Radar Communication System Based on a MIMO Radar Using OFDM Waveforms

A novel dual-function radar communication (DFRC) system is proposed, that achieves high communication rate, and can flexibly trade-off rate for improved sensing performance. The proposed system is a monostatic multiple-input multiple-output (MIMO) radar and transmits wideband, precoded, orthogonal frequency division multiplexing (OFDM) waveforms from its antennas. The system subcarriers are divided into two groups, i.e., shared and private. On a shared subcarrier, all antennas can transmit simultaneously, while on a private one only one antenna can transmit at a time. A novel target estimation approach with low complexity is proposed to overcome the coupling of transmitted symbols and radar target parameters in the target echoes, which arises due to the shared use of subcarriers by the transmit antennas. The proposed method first operates on all (shared and private) subcarriers to obtain coarse angle estimates, and then fine-tunes those estimates based on the signal received on the private subcarriers. The resolution of the coarse angle estimates is limited by the physical receive array, while the fine-tuning is enabled by effectively constructing a virtual array that has larger aperture than the receive array. The precoding matrix is optimally designed to optimize a weighted combination of the beampattern error with respect to a desirable beampattern, and the signal-to-noise ratio at the communication receiver.

Off-Grid DOA Estimation Using Sparse Bayesian Learning for MIMO Radar under Impulsive Noise.

Direction of arrival (DOA) estimation is an essential and fundamental part of array signal processing, which has been widely used in radio monitoring, autonomous driving of vehicles, intelligent navigation, etc. However, it remains a challenge to accurately estimate DOA for multiple-input multiple-output (MIMO) radar in impulsive noise environments. To address this problem, an off-grid DOA estimation method for monostatic MIMO radar is proposed to deal with non-circular signals under impulsive noise. In the proposed method, firstly, based on the property of non-circular signal and array structure, a virtual array output was built and a real-valued sparse representation for the signal model was constructed. Then, an off-grid sparse Bayesian learning (SBL) framework is proposed and further applied to the virtual array to construct novel off-grid sparse model. Finally, off-grid DOA estimation was realized through the solution of the sparse reconstruction with high accuracy even in impulsive noise. Numerous simulations were performed to compare the algorithm with existing methods. Simulation results verify that the proposed off-grid DOA method enables evident performance improvement in terms of accuracy and robustness compared with other works on impulsive noise.

On Parameter Identifiability of Diversity-Smoothing-Based MIMO Radar

Diversity smoothing has been widely used for angle estimation with multiple input multiple output (MIMO) radar in the presence of coherent or correlated targets, and the parameter identifiability is an interesting issue. Previous works have shown sufficient conditions for some special cases, such as the coherent targets with conventional MIMO radar, and the array size are derived as a function of the target number and target structure. In this article, we further introduce the interelement spacings to build a complete parameter identifiability scheme. For monostatic MIMO radars, the antenna numbers and interelement spacings of transmit and receive arrays are derived as functions of the target number and the target structure. The optimization models are built to calculate the maximum number of detectable targets for a given target structure. Additionally, the conditions for the bistatic MIMO radar are derived from two-dimension viewpoint. It is shown that the new results improve upon previous spatial smoothing or diversity smoothing methods and recover them in special cases. Simulation results are presented that corroborate our theoretical findings.

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 Target Localization Method based on Optimized Newton Algorithm for MIMO Radar

Target localization is one of the fundamental research topics in multiple input multiple output (MIMO) radar systems. In this paper, we focus on the localization parameter estimation including direction of departure (DoD), direction of arrival (DoA) and radar cross section (RCS) coefficients in monostatic MIMO radar systems. An optimized Newton algorithm is proposed to jointly estimate DoAs/DoDs and RCS coefficients. Its angle estimation performance outperforms traditional Capon, MUSIC and ESPRIT methods. The proposed localization algorithm also has great RCS coefficient estimation performance. Moreover, the joint estimation performance of the proposed optimized Newton algorithm is superior even with unknown target numbers and low sampling numbers. Simulation results verify the effectiveness of the proposed optimized Newton algorithm.

Height measurement method of monostatic metre wave multiple input multiple output radar based on coprime array

The height measurement algorithm of metre wave multiple input multiple output (MIMO) radar based on uniform linear array (ULA) has some problems, such as a sharp decline of angle measurement accuracy of ultra-low altitude targets and large fluctuation of height measurement with the change of elevation. In order to solve the above difficult problems, starting from the perspective of reducing the influence of multipath effect by array structure, coprime array (CPA) is applied to the height measurement of monostatic metre wave MIMO radar. In this paper, the signal model of monostatic CPA metre wave MIMO radar is established under the condition of flat ground, a height measurement method using physical array is proposed based on the derivation that it is infeasible to use virtual array method for height measurement, it fills the gap of low elevation estimation method based on sparse array metre wave MIMO radar. The proposed method calculates the echo data according to the characteristics of the steering vector of CPA, then obtains the accurate low elevation by using the generalised multiple signal classification algorithm, and finally the target height is obtained by using the geometric relationship. Simulation experiments compare three typical CPAs with ULA using equal array elements, and verify the superiority of height measurement performance of monostatic metre wave MIMO radar based on CPA and the effectiveness of the proposed method.

Multi-UAV Cooperative Localization for Marine Targets Based on Weighted Subspace Fitting in SAGIN Environment

As an indispensable part of the Internet of Vehicles (IoV), unmanned aerial vehicles (UAVs) can be deployed for target positioning and navigation in the space–air–ground-integrated network (SAGIN) environment. Maritime target positioning is very important for the safe navigation of ships, hydrographic surveys, and marine resource exploration. Traditional methods typically exploit satellites to locate marine targets in the SAGIN environment, and the location accuracy does not satisfy the requirements of modern ocean observation missions. In order to localize the marine target, we develop a system architecture in this article, which contains UAVs integrated with monostatic multiple-input–multiple-output (MIMO) radars. The main thrust is to estimate the direction-of-arrival (DOA) via MIMO radar. Herein, we consider a general scenario that unknown mutual coupling exist and a novel sparse reconstruction algorithm is proposed. The mutual coupling matrix (MCM) is adopted with the help of its special structure, we formulate the data model as a sparse representation form. Then, two novel matrices, a weighted matrix, and a reduced-dimensional matrix are constructed to reduce the computational complexity and enhance the sparsity, respectively. Thereafter, a sparse constraint model is constructed using the concept of optimal weighted subspace fitting (WSF). Finally, the DOA estimation of maritime targets can be achieved by reconstructing the support of a block sparse matrix. Based on the DOA estimation results, multiple UAVs are used to cross-locate marine targets multiple times, and an accurate marine target position is achieved in the SAGIN environment. Numerical results are carried out, which demonstrates the effectiveness of the proposed DOA estimator, and the multi-UAV cooperative localization system can realize accurate target localization.

DOA estimation in beamspace for monostatic MIMO radar utilizing real polynomial rooting

A novel angle estimation method in beamspace for monostatic multiple-input multiple-output (MIMO) radar based on a real polynomial rooting technique is presented. In order to eliminate the redundancy of the received data, the reduced-dimensional matrix is used to reduce the dimension of the received data. To further reduce computational complexity, the reduced-dimensional data is transformed into the beamspace. In order to avoid computationally expensive spectrum search, we will introduce conformal transformation to determine the extreme value of the MUSIC spectrum by utilizing the real polynomial rooting technique. Finally, the DOA of the target can be obtained from these extreme values. Numerical results demonstrate the effectiveness of the proposed algorithm.

Monostatic MIMO radar direction finding in impulse noise

This work considers direction-finding using a monostatic multiple-input multiple-output (MIMO) radar in the presence of impulsive noise. Employing a novel low-order covariance-based exponential kernel function, the proposed maximum likelihood (ML) formulation exploits an introduced quantum whale optimization algorithm (QWOA) to form the direction estimates. The resulting estimates are shown to be robust to the presence of impulsive noise, offering preferable performance as compared to recent related approaches, even in cases when the number of available snapshots is small.

An Improved ESPRIT-Based Algorithm for Monostatic FDA-MIMO Radar With Linear or Nonlinear Frequency Increments

This letter proposes a search-free algorithm based on ESPRIT for angle and range estimation in monostatic multiple-input multiple-output (MIMO) radar with frequency diverse array (FDA). In this algorithm, the angle is estimated by the ESPRIT algorithm, and the range is calculated by the least-squares principle. Thus, it is suited for FDA-MIMO radar works in linear and nonlinear frequency increments. Besides, the improved algorithm has better range estimation performance than the traditional ESPRIT algorithm. Simulation results prove the effectiveness of the proposed algorithm.

RxCV-based unitary SBL algorithm for off-grid DOA estimation with MIMO radar in unknown non-uniform noise

As an indispensable part of array signal processing, direction-of-arrival (DOA) estimation has been well investigated over the past few decades, and many excellent DOA estimation methods have been proposed. In this paper, a receiving domain covariance vector (RxCV) based unitary SBL algorithm is proposed for the off-grid DOA estimation of monostatic multiple-input multiple-output (MIMO) radar in unknown non-uniform noise environment. In the proposed algorithm, the data received by MIMO radar is firstly transformed into receiving domain by a reshape operation. Then the RxCV-based unitary off-grid sparse model without non-uniform noise is constructed through unitary transformation and first-order linear approximation, where the unknown non-uniform noise is got rid off by a linear transformation. Based on the RxCV-based unitary off-grid sparse model, the sparse Bayesian learning (SBL) criterion is adopted to estimate the parameters, where signal variance and off-gird error are estimated by using expectation-maximization (EM) strategy. The DOA estimation is ultimately realized through 1-dimensional spectrum search of the received data. Results of the simulation experiments have provided the evidence of that the proposed algorithm is robustness against nonuniform noise and off-grid error, and it can maintain superior DOA estimation performance compared with other reported sparse signal representation based methods.

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.

Ultracompact Monostatic MIMO Radar With Nonredundant Aperture

Monostaticmultiple-input–multiple-output (MIMO) radar configurations using the same antenna array for transmission and reception inherently lead to redundant virtual antenna positions within the virtual MIMO aperture. This article presents a new method for designing monostatic MIMO radars where this disadvantage may be overcome and nonredundant virtual arrays are yielded by exploiting biomimetic antenna arrays (BMAAs). The new concept of biomimetic MIMO is introduced and thoroughly investigated in this article. Its working principle is compared with conventional monostatic MIMO and verified by radar measurements. The new approach is intended to improve the angle estimation capability of, in particular, small radar systems. Therefore, the concept is applied to an ultracompact two-channel radar monolithic microwave integrated circuit (MMIC) with fully integrated antennas for range and angle measurements in the 150-GHz range. The chip features two monostatic transmit–receive (TRX) channels with an antenna spacing of $\lambda /4$ and occupies less than 3 mm2 of space. By applying biomimetic MIMO, a virtual aperture consisting of four unique antenna positions and an equivalent total size of around $1.5\lambda $ is achieved.

A Cauchy score DOA estimator for monostatic MIMO radar in impulsive noise environment

ABSTRACT It is interesting to determine the direction of arrival (DOA) of signals in the presence of impulsive noise for monostatic multiple-input multiple-output (MIMO) radar. Inspired by the robust statistics property of M-estimation theory and maximum likelihood estimation theory, a Cauchy score cost function is proposed to suppress the outliers, leading to robust DOA estimation. Firstly, the Cauchy score cost function for residual fitting error matrix of the radar data is derived. Then, an alternating convex optimisation algorithm based on the complex-valued Newton gradient descent is tailored to acquire an efficient solution for the Cauchy score cost function. Finally, using the signal subspace matrix obtained by the alternating convex optimisation algorithm, ESPRIT algorithm is adopted to provide a closed-form DOA estimations. Simulation results are presented to demonstrate the effectiveness of the proposed method.

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.

A Novel Unitary ESPRIT Algorithm for Monostatic FDA-MIMO Radar.

A novel unitary estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm, for the joint direction of arrival (DOA) and range estimation in a monostatic multiple-input multiple-output (MIMO) radar with a frequency diverse array (FDA), is proposed. Firstly, by utilizing the property of Centro-Hermitian of the received data, the extended real-valued data is constructed to improve estimation accuracy and reduce computational complexity via unitary transformation. Then, to avoid the coupling between the angle and range in the transmitting array steering vector, the DOA is estimated by using the rotation invariance of the receiving subarrays. Thereafter, an automatic pairing method is applied to estimate the range of the target. Since phase ambiguity is caused by the phase periodicity of the transmitting array steering vector, a removal method of phase ambiguity is proposed. Finally, the expression of Cramér–Rao Bound (CRB) is derived and the computational complexity of the proposed algorithm is compared with the ESPRIT algorithm. The effectiveness of the proposed algorithm is verified by simulation results.

Joint Angle Estimation and Array Calibration Using Eigenspace in Monostatic MIMO Radar

In this article, aiming at the presence of the unknown gain-phase uncertainties in monostatic multiple-input multiple-output (MIMO) radar, an eigenspace based algorithm for joint parameter estimation is proposed to achieve angle and array calibration. The initial estimation of the direction of arrival (DOA) can be obtained from the signal subspace achieved by the eigenvalue decomposition (EVD) of covariance matrix, and then an improved multiple signal classification (MUSIC)-based cost function is established for achieving more accurate estimation of DOA, in which a local searching is only required because of the initial estimation value. Eventually, with the help of the estimated DOAs and the noise subspace, the error vector of array gain-phase can be achieved. The proposed method is not only superior to the classical subspace-based algorithms in terms of angle and array gain-phase error estimation, such as MUSIC-like method, especially for closely-spaced targets, but also can be suitable for non-uniform linear arrays (non-ULA). What's more, the computational complexity of the proposed method can be considerably reduced since only a local searching is needed. Multiple simulation experiments are carried out to illustrate the performance of the proposed scheme.

CRBs for direction‐of‐departure and direction‐of‐arrival estimation in collocated MIMO radar in the presence of unknown spatially coloured noise

This study gives explicit closed-form expressions of the stochastic Cramer–Rao bounds (STO-CRBs) on direction-of-departure and direction-of-arrival estimation accuracies for collocated multiple-input–multiple-output (MIMO) radar with unknown spatially coloured noise. In some special cases, i.e. the CRB of direction estimation accuracy for monostatic MIMO radar, the white noise scenario is discussed. Theoretical comparisons between the STO-CRBs and the deterministic ones are presented. Finally, these bounds are numerically compared.

The Effect of Antenna Mutual Coupling on MIMO Radar System Performance

Multiple-input multiple-output (MIMO) radar systems use orthogonal signaling to enable combined transmit and receive beamforming through signal processing on the received signals. Because of the combined role of transmit and receive arrays in this processing for monostatic MIMO radar, mutual coupling present in these arrays impacts the beamforming in a unique way. This paper presents a mathematical model of a MIMO radar with coupled antennas and appropriate radio frequency circuitry. Application of the model to evaluate radar detection of a single target demonstrates that coupling can result in target detection angles that are in error by as much as 12° and increase beamformer response sidelobe levels by as much as 8 dB. The results also show that a simple matching network can improve detection performance, particularly when array mutual coupling is high.