Multiple-input Multiple-output Radar Research Articles

Joint DOD and DOA Estimation of Bistatic MIMO Radar for Coprime Array Based on Array Elements Interpolation

This paper proposes a method to address the problem of the joint direction of departure (DOD) and direction of arrival (DOA) estimation with augmented coprime array (CPA) bistatic multiple input multiple output (MIMO) radar using interpolating sensors. At first, we deduce the regular pattern of hole positions in the virtual array and interpolate a small number of sensors to augmented CPA to form a partially contiguous virtual array. Then, we use the diversity smoothing algorithm to reconstruct the Toeplitz matrix to obtain a spatial smoothing matrix. Finally, we combine the RD-MUSIC algorithm with the spatial smoothing matrix to estimate the spatial spectrum and achieve automatic matching of DODs and DOAs for the targets. Simulation results clearly illustrate the superiority of the method.

Phase synchronization sensitivity for widely separated MIMO radar in CES disturbance

This paper investigates the sensitivity to phase synchronization errors through the Hybrid Cramér–Rao Lower Bounds (HCRLB) for joint estimation of target location and velocity in coherent Multiple-Input Multiple-Output (MIMO) radars with distributed antennas. In particular, we derived the HCRLB of range and velocity of a target embedded in Complex Elliptically Symmetric (CES) distributed clutter when the phase error on each receiver and transmitter is modeled as a Gaussian random variable. Closed-form expressions of the HCRLBs are derived and evaluated as a function of the phase error variance.

A Multi-Objective Quantum Genetic Algorithm for MIMO Radar Waveform Design

Aiming at maximizing waveform diversity gain when designing a phase-coded multiple-input multiple-output (MIMO) radar waveform set, it is desirable that all waveforms are orthogonal to each other. Hence, the lowest possible peak cross-correlation ratio (PCCR) is expected. Meanwhile, low peak auto-correlation side-lobe ratio (PASR) is needed for good detection performance. However, it is difficult to obtain a closed form solution to the waveform set from the expected values of the PASR and PCCR. In this paper, the waveform set design problem is modeled as a multi-objective, NP-hard constrained optimization problem. Unlike conventional approaches that design the waveform set through optimizing a weighted sum objective function, the proposed optimization model evaluates the performance of multi-objective functions based on Pareto level and obtains a set of Pareto non-dominated solutions. That means that the MIMO radar system can trade off each objective function for different requirements. To solve this problem, this paper presents a multi-objective quantum genetic algorithm (MoQGA) based on the framework of quantum genetic algorithm. A new population update strategy for the MoQGA is designed based on the proposed model. Compared to the state-of-the-art methods, like BiST and Multi-CAN, the PASR and PCCR metrics of the waveform set are 0.95–3.91 dB lower with the parameters of the numerical simulation. The MoQGA is able to minimize PASR and PCCR of the MIMO radar waveform set simultaneously.

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.

A novel co-prime MIMO radar model for DOA estimation

In this paper, we propose a novel co-prime multiple-input multiple-output (MIMO) radar model based on the transmit energy focusing (TEF) technique for direction of arrival (DOA) estimation. Compared with omnidirectional radiation of the transmit energy of the traditional co-prime MIMO radar, the proposed model can focus the transmit energy in the desired spatial sector. To obtain more degrees of freedom (DOFs), we design the transmit beamspace matrix with a specific structure. Then, the sum and difference co-array (SDC) is given by vectorizing the sampling covariance matrix of the received data. Subsequently, the maximum number of consecutive lags and unique lags for SDC is derived. Besides, we also derive the Cramer–Rao bound (CRB) of our model. Moreover, the DOA estimation performance of the TEF-based co-prime MIMO radar and traditional co-prime MIMO radar is compared by using forward-backward spatial smoothing (FBSS), sparse recovery (SR), and fast iterative interpolated beamformer (FIIB) algorithms. Simulation results show the superiority of the proposed model.

MIMO Radar Beampattern Design Based on Manifold Optimization Method

The transmit beampattern design with constant modulus (CM) constraint is a key technical issue in Multiple-Input Multiple-Output (MIMO) radar system. The problem is non-convex and direct solution is impossible in general, due to the CM constraint. Usually, most existing methods indirectly solve this problem by relaxing it to a more tractable form. These methods usually either degrade the performance due to relaxation or consume expensive computational cost. Different from the relaxation methods, a direct method is proposed to solve the non-convex problem without relaxation. In the proposed method, the original problem is first converted to an Unconstrained Quadratic Lagrange Multiplier Problem (UQLMP) over the non-convex complex circle manifold. Then, by efficiently finding the descent direction and step size to ensure the objective function to be non-increasing, a novel Manifold Optimization-based Conjugate Gradient (MOCG) approach is derived to directly solve UQLMP. Compared with current methods, the proposed method has better performance in terms of nulling depth.

Efficient Gridless Angle Estimation for Bistatic MIMO Radar With Planar Arrays

Direction finding in bistatic multiple-input multiple-output (MIMO) radar system is an important issue in target localization. Existing works focus on linear array which can only find 1-D angle information. In this paper, we focus on bistatic MIMO radar where both the transmitter and receiver are equipped with uniform planar arrays (UPAs) which can find full angle information of targets. To overcome the gridding effect, we use atomic norm minimization (ANM) to formulate a gridless model which is equivalent to a semidefinite programming (SDP). By solving the SDP we can obtain the angle estimates without discretizing the 4-D angle space. An alternating direction method of multipliers (ADMM) based implementation is also derived to reduce the computational complexity of the proposed method. Simulation results show that our proposed method can break through the gridding limitation to achieve a high estimation accuracy while enjoying low computational complexity.

Online Sparse DOA Estimation Based on Sub–Aperture Recursive LASSO for TDM–MIMO Radar

The least absolute shrinkage and selection operator (LASSO) algorithm is a promising method for sparse source location in time–division multiplexing (TDM) multiple–input, multiple–output (MIMO) radar systems, with notable performance gains in regard to resolution enhancement and side lobe suppression. However, the current batch LASSO algorithm suffers from high–computational complexity when dealing with massive TDM–MIMO observations, due to high–dimensional matrix operations and the large number of iterations. In this paper, an online LASSO method is proposed for efficient direction–of–arrival (DOA) estimation of the TDM–MIMO radar based on the receiving features of the sub–aperture data blocks. This method recursively refines the location parameters for each receive (RX) block observation that becomes available sequentially in time. Compared with the conventional batch LASSO method, the proposed online DOA method makes full use of the TDM–MIMO reception time to improve the real–time performance. Additionally, it allows for much less iterations, avoiding high–dimensional matrix operations, allowing the computational complexity to be reduced from OK3 to OK2. Simulated and real–data results demonstrate the superiority and effectiveness of the proposed method.

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.

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.

Low elevation and range joint estimation method with metre wave frequency diverse array‐multiple input multiple output radar based on sparse array

Frequency diverse array-multiple input multiple output (FDA-MIMO) radar not only has the advantage of waveform diversity but also can realise azimuth-range correlation map, so it can realise high precision azimuth-range joint estimation and has the ability to distinguish different range unit targets in the same azimuth. However, the existing research mainly adopts uniform linear array (ULA) model and mainly focusses on incoherent targets. At present, the elevation-range joint estimation algorithm of metre wave FDA-MIMO radar based on ULA has the problems of sharp decline in angle-range measurement accuracy of ultra-low altitude targets and large fluctuation of elevation-range measurement accuracy with changing elevation. In order to solve the above difficult problems, starting from the perspective of reducing the influence of multipath effect by array structure, this paper innovatively combines sparse array with FDA-MIMO radar, establishes the low elevation-range joint estimation model of metre wave FDA-MIMO radar based on sparse array, and puts forward the low elevation-range joint estimation method suitable for this model, and it fills the gap of low elevation-range measurement method based on sparse array metre wave FDA-MIMO radar. Simulation experiments compare two typical sparse arrays with ULA of equal array elements, which verify the superiority of low elevation-range measurement performance of monostatic metre wave FDA-MIMO radar based on sparse array and the effectiveness of the proposed method.

Transceiver coexistence design of MIMO radar and MIMO communication under Gaussian model uncertainty

This paper proposes a transceiver coexistence design of multiple-input multiple-output (MIMO) radar and MIMO communication under Gaussian model uncertainty. To tackle the mutual interference and the model uncertainty problems, the transceivers of the radar system and the communication system are jointly designed to minimise the total transmit power of the radar system and the communication system, while the signal-to-interference-plus-noise-ratios (SINRs) or effective SINR (ESINR) of the radar system and the mean square error (MSE) of the communication system are guaranteed with small outage probability. Safe approximations are proposed for the probabilistic constraints, and a converged iterative algorithm is proposed for the joint transceiver design. Owing to the MSE formulation, the convex relaxation for the communication transmitter is proved to be tight. Furthermore, the tight communication relaxation provides an effective way to check whether the radar error is majorly due to numeric error or the possible rank-one relaxation error. Extensive simulation results show that the proposed iterative algorithm converges, the possible radar rank-one relaxation error is trivial with respect to the numeric error, and the quality of service in terms of SINR, ESINR, MSE and bit error rate (BER) in the proposed coexistence system are robust against model uncertainty with the cost of using extra transmit power.

Coherent Measurements of a Multistatic MIMO Radar Network With Phase Noise Optimized Non-Coherent Signal Synthesis

For multistatic radar networks in the upper mm-wave range with a large spacing between its radar sensor nodes, a coherent signal distribution is very complex and thus very costly. Hence, it is desirable to generate the mm-wave signals individually for each radar sensor node, i.e., non-coherently. However, multistatic radar networks using a non-coherent signal distribution for its radar sensor nodes are affected by systematic errors and uncorrelated phase noise, which reduces the resolution and the detection performance of these systems. In this article, a novel non-coherent signal synthesis concept based on the direct digital synthesis (DDS) principle is presented for multistatic radar networks. Compared to a signal synthesis using a phase-locked loop (PLL), it is shown that the different phase noise behavior of the DDS is beneficial for bistatic signal paths between the radar sensor nodes. The presented hardware concept is considered and analyzed for three different types of coherency regarding the signal distribution: coherent, quasi-coherent, and incoherent. Measurements with a multiple-input multiple-output (MIMO) radar at <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$150 \,\mathrm{G}\mathrm{Hz}$</tex></formula> prove that despite a non-coherent signal distribution, it is possible to achieve the same detection and imaging performance as with a fully coherent radar by using a DDS.

A Deep Learning Based Super Resolution DoA Estimator With Single Snapshot MIMO Radar Data

In this article, we study the radar angular resolution, the capability of distinguishing multiple targets in different directions of arrivals (DoA). We present a deep learning-based super resolution DoA estimator for multiple input multiple output (MIMO) radar with single snapshot data. The estimator consists of a deep learning DoA classifier (DLDC) in the central bearing angle zone, which can simultaneously detect up to 11 targets in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$[ { - 2^\circ,\ 2^\circ } ]$</tex-math></inline-formula> with 32 virtual antenna elements, and a spatial filtering pre-rotator (SFPR) that makes the DLDC supporting any bearing angle location in a wider radar field of view (FoV). We present the DLDC-SFPR structure and provide detailed parameter setting. In the performance evaluation, the resolution of proposed method is first compared with the theoretical angular resolution limit (ARL). It is shown that the proposed estimator reaches the Chernoff ARL. The probability of resolution (PoR) is then investigated and compared with typical and state-of-the-art DoA estimators in various conditions. The numerical results show that the DLDC-SFPR achieves 0.4 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> resolution with 90% PoR to distinguish 2 targets at signal to noise ratio (SNR) of 7 dB if the targets are on-gird. In the case of any-angle, the same resolution and probability are achieved at SNR = 12 dB. The proposed method is robust to multi-target concurrent cases, and outperforms the existing reported approaches. Two procedures, uniform scanning and progressive scanning, are proposed for wider FoV applications.

W‐Band 76–81 GHz Millimeter‐Wave Comb‐Line Array for Automotive Short Range Radar

AbstractIn this paper, we design a W‐band millimeter‐wave antenna for the band of 76–81 GHz for frequency modulated continuous wave (FMCW) multiple input multiple output radar systems in automotive applications. It exhibits radiation patterns that possess narrow beamwidths with minimal beam squint in the elevation plane within the band for detection of vehicles and pedestrians in the forward direction instead of objects on the pavement and above the horizon, as well as a wide beamwidth in the horizontal plane, all as required by automotive radar antennas. A four‐port array of comb‐line antennas with diagonally tilted stubs is herein designed, the latter feature for mitigation of interruption from oncoming vehicles traveling along the opposite direction. In order to suppress the mutual interference between multiple antennas in the array, an electromagnetic band gap (EBG) structure is designed to enhance the isolation between ports. A prototype of the design was manufactured and tested, yielding results that satisfy the design requirements.

Multi-Target Localization of MIMO Radar with Widely Separated Antennas on Moving Platforms Based on Expectation Maximization Algorithm

This paper focuses on multi-target parameter estimation of multiple-input multiple-output (MIMO) radar with widely separated antennas on moving platforms. Aiming at the superimposed signals caused by multi-targets, the well-known expectation maximization (EM) is used in this paper. Target’s radar cross-section (RCS) spatial variations, different path losses and spatially-non-white noise appear because of the widely separated antennas. These variables are collectively referred to as signal-to-noise ratio (SNR) fluctuations. To estimate the echo delay/Doppler shift and SNR, the Q function of EM algorithm is extended. In addition, to reduce the computational complexity of EM algorithm, the gradient descent is used in M-step of EM algorithm. The modified EM algorithm is called generalized adaptive EM (GAEM) algorithm. Then, a weighted iterative least squares (WILS) algorithm is used to jointly estimate the target positions and velocities based on the results of GAEM algorithm. This paper also derives the Cramér-Rao bound (CRB) in such a non-ideal environment. Finally, extensive numerical simulations are carried out to validate the effectiveness of the proposed algorithm.

Joint waveform and precoding design for coexistence of MIMO radar and MU‐MISO communication

The joint design problem for the coexistence of multiple-input multiple-output (MIMO) radar and multi-user multiple-input-single-output (MU-MISO) communication is investigated. Different from the conventional design schemes, which require defining the primary function, we consider designing the transmit waveform, precoding matrix and receive filter to maximize the radar SINR and the minimal SINR of communication users, simultaneously. By doing so, the promising overall performance for both sensing and communication is achieved without requiring parameter tuning for the threshold of communication or radar. However, the resulting optimization problem which contains the maximin objective function and the unit sphere constraint, is highly nonconvex and hence difficult to attain the optimal solution directly. Towards this end, the epigraph-form reformulation is first adopted, and then an alternating maximisation (AM) method is devised, in which the Dinkelbach’s algorithm is used to tackle the nonconvex fractional-programing subproblem. Simulation results indicate that the proposed method can achieve improved performance compared with the benchmarks.

CRLBs for Location and Velocity Estimation for MIMO Radars in CES-Distributed Clutter

In this article, we investigate the problem of jointly estimating target location and velocity for widely separated multiple-input multiple-output (MIMO) radar operating in correlated non-Gaussian clutter, modeled by a complex elliptically symmetric (CES) distribution. More specifically, we derive the Cramér–Rao lower bounds (CRLBs) when the target is modeled by the Swerling 0 model and the clutter is complex t-distributed. We thoroughly analyze the impact of the clutter correlation and spikiness to provide accurate performance estimation. Index terms—Cramér–Rao lower bounds (CRLBs), MIMO radar, location and velocity estimation, performance analysis, complex elliptically symmetric (CES) distributed, and complex t-distribution.

Range Gate Pull-Off Mainlobe Jamming Suppression Approach with FDA-MIMO Radar: Theoretical Formalism and Numerical Study

With the development of an electronic interference technique, the self-defense jammer can generate mainlobe jamming using the range gate pull-off (RGPO) strategy, which brings serious performance degradation of target tracking for the ground-based warning radar. In this paper, a RGPO mainlobe jamming suppression approach is proposed, with a frequency diverse array using multiple-input multiple-output (FDA-MIMO) radar. The RGPO mainlobe jamming differs from the true target in slant range, thus it is possible to identify the true target from the RGPO mainlobe jammings by exploiting the transmit beampattern diversity of FDA-MIMO radar. A RGPO mainlobe jamming suppression approach is devised by using joint transmit–receive beamforming for a group of range sectors. The jamming suppression performance is studied, in consideration of practical time-delay of RGPO jamming. Simulation examples are provided to verify the effectiveness of the proposed approach.

Joint Angle and Range Estimation in Monostatic FDA-MIMO Radar via Compressed Unitary PARAFAC

In this paper, we study the joint range and angle estimation problem based in monostatic frequency diverse-array multiple-input multiple-output (FDA-MIMO) radar, and propose a method for range and angle estimation base on compressed unitary parallel factor (PARAFAC). First, the received complex signal matrix is stacked into a third-order complex signal tensor. Then, we can transform the obtained third-order complex signal tensor into a third-order real-valued signal tensor by employing forward–backward and unitary transformation techniques. Next, a smaller third-order real-valued signal tensor is composed by using compressing the third-order real-valued signal tensor. After that, PARAFAC decomposition is applied to obtain the direction matrix. Lastly, the angle and range are estimated by employing the least square (LS) fitting. The estimation error of the proposed method is about 10% lower than that of the traditional PARAFAC method under the low number of snapshots. When the number of snapshots is high, the performance of the two methods is close. Moreover, the computational complexity of the proposed method is nearly 96% less than those of the traditional PARAFAC methods in the case of low snapshots, while the gap is larger in the case of high snapshots. The superiority and effectiveness of the method are proved by complexity analysis and simulation experiments.