Multiple-input Multiple-output Radar System Research Articles

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.

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.

Transmitter selection and receiver placement for target parameter estimation in cooperative radar‐communications system

Target parameter estimation is considered for the cooperative multiple-input multiple-output (MIMO) radar and MIMO communications system. To address the hardware limitation of the radar system, a joint transmitter selection and receiver placement (JTSRP) problem is formulated to minimise the estimation performance benchmarked by the Cramer–Rao bound (CRB), where the transmitters can be selected from a discrete set and the receivers can be deployed over a continuous region. To efficiently solve this mix-integer non-linear programming problem approximately, a genetic algorithm (GA)-based method is proposed. It is shown that the result obtained by the proposed algorithm is close enough to the optimum solution of the JTSRP problem. Numerical examples are presented to analyse the performance of the cooperative system designed by the GA-based JTSRP.

Efficient Through-Wall Human Pose Reconstruction Using UWB MIMO Radar

In this letter, we introduce ultrawideband (UWB) Pose, a through-wall human pose reconstruction framework using UWB multiple-input--multiple-output (MIMO) radar. Previous radio frequency-based works have achieved two-dimensional (2-D) and 3-D human pose reconstruction in human detection of occlusion scenes. However, these methods are difficult to adapt to the environment and often suffer from high computational complexity. To address this issue, we first construct 3-D radar images of human targets using UWB MIMO radar system, and transform those radar images into discrete 3-D point data. Then, design a lightweight deep learning network to extract human body features from the input point data, finally convert the features into 3-D pose coordinates. The comparative experimental results show that the human pose reconstruction error of our UWB-Pose framework can be as low as 38.84 mm. Importantly, the number of parameters and floating point operations are further reduced to 1.02 M and 2.75 G, to meet the needs in practical deployments and the scope of applicability.

Human Activity Signatures Captured under Different Directions Using SISO and MIMO Radar Systems

In this paper, we highlight and resolve the shortcomings of single-input single-output (SISO) millimeter wave (mm-Wave) radar systems for human activity recognition (HAR). A 2×2 distributed multiple-input multiple-output (MIMO) radar framework is presented to capture human activity signatures under realistic conditions in indoor environments. We propose to distribute the two pairs of collocated transmitter–receiver antennas in order to illuminate the indoor environment from different perspectives. For the proposed MIMO system, we measure the time-variant (TV) radial velocity distribution and TV mean radial velocity to observe the signatures of human activities. We deploy the Ancortek SDR-KIT 2400T2R4 mm-Wave radar in a SISO as well as a 2×2 distributed MIMO configuration. We corroborate the limitations of SISO configurations by recording real human activities in different directions. It is shown that, unlike the SISO radar configuration, the proposed MIMO configuration has the ability to obtain superior human activity signatures for all directions. To signify the importance of the proposed 2×2 MIMO radar system, we compared the performance of a SISO radar-based passive step counter with a distributed MIMO radar-based passive step counter. As the proposed 2×2 MIMO radar system is able to detect human activity in all directions, it fills a research gap of radio frequency (RF)-based HAR systems.

Transmit-Receive Beamforming for Distributed Phased-MIMO Radar System

Distributed phased Multiple-Input Multiple-Output (phased-MIMO) radar system consisting of separated subarrays possesses a high angle resolution but with sharing high sidelobes. This paper therefore focuses on the design of a virtual low-sidelobe beampattern in distributed phased-MIMO radar. An iterative framework with respect to subarray layout and receive weighting coefficients is proposed to minimize beampattern Peak Sidelobe Level (PSL) accounting for practical position constraints as well as the mainlobe level restriction. In each iteration, an efficient cyclic optimization algorithm based on the Coordinate Descent (CD) framework is developed to seek for the subarray layout through splitting high dimensional layout optimization problem into multiple one-dimensional optimization problems, and the SemiDefinite Relaxation (SDR) technique and rank one approximation are explored to design weighting coefficients. Numerical simulations are provided to assess the performance of the proposed algorithms in term of the achieved beampattern under different constraints and parameter settings.

Joint Design for Cooperative Radar and Communication Systems in Multi-Target Optimization

In this brief, a joint design for cooperative spectrum sharing framework of multiple-input multiple-output (MIMO) radar and MIMO communication system in radar multi-target optimization is proposed. The interesting design Degrees of Freedom (DOFs) include radar transceiver beamforming vector and communication system codebook, whereby the designed problem is a constrained minimization of total radar effective interference power under power limited conditions and communication rate constraint guaranteeing both the two coexistence systems performance. As to the non-convex multi-variables problem, an alternating iteration approach is developed to solve the three suitably transformed subproblems, the closed form solutions are derived, and the convergence of the proposed design is guaranteed. Finally, numerical results show the spectrum sharing performance of the proposed approaches.

Joint linear array structure and waveform design for MIMO radar under practical constraints

<abstract> <p>Optimizing the array structure or emission waveform of a multiple-input multiple-output (MIMO) radar system is an effective method to improve the performance in practical applications. In this study, the joint optimization of array structure and the corresponding emission waveform under interference and noise conditions was investigated. When compared with the waveform or array structure optimization alone, this method allowed the MIMO radar system to obtain a higher degree of freedom. By considering the practical limitations of the MIMO radar system, a waveform with good properties, such as orthogonality or pulse compression performance, was selected as the reference waveform. Subsequently, based on the similarity constraint and constant modulus constraint, a bivariate joint optimization problem of array structure and waveform was formulated, and an iterative optimization algorithm was proposed to solve it. The array composition was determined using a combinatorial search algorithm while the emission waveform was obtained by solving the similarity model. Eventually, it effectively converged to form quasi-optimal match variables after limited iterations. The proposed method can be expanded to the optimal launch of a specific target and an environment with a proper or minimum number of antennas, as well as implement array optimization with the desired waveform. The simulation results prove the effectiveness of the proposed method. This method provides an ideal choice for real-time construction, flexible launch, and signal processing of MIMO radar systems.</p> </abstract>

MIMO Radar Transmit Beampattern Shaping for Spectrally Dense Environments

Designing unimodular waveforms with a desired beampattern, spectral occupancy and orthogonality level is of vital importance in the next generation Multiple-Input Multiple-Output (MIMO) radar systems. Motivated by this fact, in this paper, we propose a framework for shaping the beampattern in MIMO radar systems under the constraints simultaneously ensuring unimodularity, desired spectral occupancy and orthogonality of the designed waveform. In this manner, the proposed framework is the most comprehensive approach for MIMO radar waveform design focusing on beampattern shaping. The problem formulation leads to a non-convex quadratic fractional programming. We propose an effective iterative to solve the problem, where each iteration is composed of a Semi-Definite Programming (SDP) followed by eigenvalue decomposition. Some numerical simulations are provided to illustrate the superior performance of our proposed over the state-of-the-art.

Code Optimization and Angle-Doppler Imaging for ST-CDM LFMCW MIMO Radar Systems

We consider code optimization and angle-Doppler imaging for slow-time code division multiplexing (ST-CDM) linear frequency-modulated continuous-wave (LFMCW) multiple-input multiple-output (MIMO) radar systems. We optimize the slow-time code via the minimization of a Cramér-Rao Bound (CRB)-based metric to enhance the parameter estimation performance. Then, a computationally efficient RELAX-based algorithm is presented to obtain the maximum likelihood (ML) estimates of the target angle-Doppler parameters. Numerical examples show that the proposed approaches can be used to improve the performance of parameter estimation and angle-Doppler imaging of ST-CDM LFMCW MIMO radar systems.

Sparse Parameter Estimation and Imaging in mmWave MIMO Radar Systems With Multiple Stationary and Mobile Targets

This work conceives novel target detection and parameter estimation schemes in millimeter-wave (mmWave) multiple-input multiple-output (MIMO) radar (mMR) systems for both stationary and mobile targets/radar platform. Initially, the orthogonal matching pursuit (OMP)-based mmR (OmMR) algorithm is proposed for stationary targets to estimate their radar cross-section (RCS) coefficients, angle, range locations together with the number of targets. Next, mMR systems with mobile targets and platform are considered, followed by development of the simultaneous OMP (SOMP)-based mMR (SmMR) algorithm for RCS, angle/range estimation together with their Doppler velocities. The proposed algorithms lead to a significant improvement in performance since they exploit the inherent sparsity of the mMR scattering scene in contrast to the conventional schemes. Two-dimensional (2D) mMR imaging procedures are also presented for both scenarios in the angle, range, and Doppler dimensions. Analytical expressions are derived for the Cramér-Rao bounds (CRBs) for the mean-squared error (MSE) of joint estimation of the RCS coefficients and Doppler velocities. Simulation results demonstrate that proposed schemes perform well even in low signal-to-noise ratio (SNR) scenarios with a few snapshots of the scattering environment and yield improved performance in comparison to existing sparse as well as non-sparse schemes.

Efficient Solutions for MIMO Radar Localization Under Unknown Transmitter Positions and Offsets

Two efficient solutions are proposed for the localization problem of an object in multiple-input multiple-output (MIMO) radar systems, when the transmitter positions and offsets are unknown. The localization problem is first recast into a convex form by applying the semidefinite relaxation (SDR) technique, the solution of which converges to global optimum. We also propose a closed-form solution warranting global convergence, in which the object position is estimated by two stages. In the stage-one solution, the auxiliary variables are introduced to transform the nonlinear problem into a linear form. The stage-two solution is further designed to refine the estimates obtained from the stage-one solution. The minimum number of receivers and the complexity are also analyzed for the proposed solutions. The simulated results show that the SDR and closed-form solutions provide good estimates for the object position and transmitter positions. Their performance can sufficiently approach the Cramér-Rao Lower Bound (CRLB) accuracy at the high signal-to-noise ratio (SNR).

Transmit Antenna Placement Based on Graph Theory for MIMO Radar Detection

Optimal antenna placement for target detection using multiple-input multiple-output (MIMO) radar system with widely separated antennas is studied in this paper. Considering the problem of placing a limited number of transmit antennas in a given space to maximize the detection performance, an optimization problem is formulated with the output signal-to-noise ratio (SNR) indicator as the performance metric. The initial problem is a nonlinear integer programming problem, and with the increase of the number of antennas, the computational complexity using traditional methods can be huge. In this paper, we discretize the feasible space into several grid points and employ graph theory to handle the problem. The antennas and grid points can be viewed as vertices in a bipartite graph, so that the antenna placement problem can be viewed as seeking for a proper matching between two vertex sets. We transform the problem into a stable matching (SM) problem and a maximum weighted matching (MWM) problem respectively, and propose Gale-Shapley (GS)-based and Kuhn-Munkres (KM)-based algorithms to deal with them with polynomial complexity. Numerical examples are provided to evaluate the effectiveness of the proposed methods.

Parameter Estimation in PMCW MIMO Radar Systems With Few-Bit Quantized Observations

We consider the problem of target parameter estimation in phase modulated continuous wave (PMCW) multiple-input multiple-output (MIMO) radar systems with quantized observations. We derive the Cramér-Rao bound (CRB) for jointly estimating targets’ amplitudes, time delays, Doppler shifts, and directions. The derived bound provides an efficient method to analyze the estimation performance achieved by different quantization schemes. We also devise the maximum likelihood (ML) estimator for the considered parameters, and a direct grid-based method (DGM) is introduced to obtain the ML estimates. To obtain the ML estimates more efficiently, a two-stage scheme is proposed. In the proposed scheme, we first formulate the estimation problem as a sparse signal recovery problem and modify the sparse learning via iterative minimization (SLIM) approach to solve it. Next, a RELAX-based iterative algorithm is proposed to refine the estimates. Simulation results show that the proposed scheme can approach the CRB. Simulation results also show that using quantized observations does not affect the estimation performance significantly when the targets’ amplitudes are similar.

A Robust TSWLS Localization of Moving Target in Widely Separated MIMO Radars

In this paper, we investigate the target localization problem in a Multiple-Input Multiple-Output (MIMO) radar systems with Widely Separated Antennas (WSA). We derive an accurate and robust closed-form estimator for the target's location and velocity by using the well-known Two-Stage Weighted Least Squares (TSWLS) technique. We define the nuisance variables in the first stage to obtain a set of pseudo-linear equations and solve them by the WLS estimator. We then approximate the nuisance variables with the first-order Taylor series around the estimates from the previous stage in order to reformulate a set of linear equations which is solved again using the WLS estimator. Unlike the state-of-the-art methods, the proposed method is robust against the presence of incorrect Doppler Shift (DS) measurements and perturbations errors imposed by the linear approximations. Simulation results demonstrate that our method outperforms the state-of-the-art methods not only in performance and complexity but also in robustness.

A MIMO Radar System based on Fractal Antenna Arrays for Level Measurement Applications

Abstract. In this contribution, the design of a multiple-input multiple-output (MIMO) radar system in 77–81 GHz range with 18 transmitting antennas and 24 receiving antennas for measuring the height profile of bulk solids in silos, is presented and discussed. The antenna array topologies are optimized by utilizing space filling fractals in order to approximate a circular shaped antenna array on a hexagonal grid. The proposed MIMO radar system achieves an angular resolution of 3.1∘ for a maximum scanning angle of ±45∘ and a side lobe suppression of 12.6 dB. The performance of the system has been evaluated by test measurements on a sand heap, showing an improved measurement accuracy compared to conventional radar level systems.

Orthogonal anti-jamming waveform design with extended Doppler tolerance based on the LFM-PC signal

Designing orthogonal waveforms based on linear frequency modulation and phase-coded (LFM-PC) signals with extended Doppler tolerance plays a vital role in multiple-input multiple-output (MIMO) radar systems. However, it is difficult to address the major problem of the mixed nonconvex unimodulus and nonlinear fourth-order polynomial constraints in sequence set design. In this paper, an orthogonal waveform based on the LFM-PC signal is designed for the discrete phase-coded sequence (DPCS) and continuous phase-coded sequence (CPCS). The hybrid alternating direction method of multipliers (ADMM) and coordinate descending (CD) algorithm are applied to the DPCS design. A novel method is designed with the approach of ADMM and the Lagrange programming neural network (LPNN) algorithm for the CPCS. The specific objective of this study is to propose new chirplike phase codes and a new algorithm framework that can separate the complex nonconvex and nonlinear constraints by ADMM, and we transfer the fourth-order polynomials from the nonlinear constraints into the Lagrange function. As a result, the optimized variables are updated in the ADMM-CD and ADMM-LPNN frameworks. Numerical simulation examples indicate the good performance of the proposed methods, and we compare these two effective methods.

An Overdispersed Black-Box Variational Bayesian-Kalman Filter with Inaccurate Noise Second-Order Statistics.

Aimed at the problems in which the performance of filters derived from a hypothetical model will decline or the cost of time of the filters derived from a posterior model will increase when prior knowledge and second-order statistics of noise are uncertain, a new filter is proposed. In this paper, a Bayesian robust Kalman filter based on posterior noise statistics (KFPNS) is derived, and the recursive equations of this filter are very similar to that of the classical algorithm. Note that the posterior noise distributions are approximated by overdispersed black-box variational inference (O-BBVI). More precisely, we introduce an overdispersed distribution to push more probability density to the tails of variational distribution and incorporated the idea of importance sampling into two strategies of control variates and Rao–Blackwellization in order to reduce the variance of estimators. As a result, the convergence process will speed up. From the simulations, we can observe that the proposed filter has good performance for the model with uncertain noise. Moreover, we verify the proposed algorithm by using a practical multiple-input multiple-output (MIMO) radar system.

Interchannel Interference and Mitigation in Distributed MIMO RF Sensing.

In this paper, we analyze and mitigate the cross-channel interference, which is found in multiple-input multiple-output (MIMO) radio frequency (RF) sensing systems. For a millimeter wave (mm-Wave) MIMO system, we present a geometrical three-dimensional (3D) channel model to simulate the time-variant (TV) trajectories of a moving scatterer. We collected RF data using a state-of-the-art radar known as Ancortek SDR-KIT 2400T2R4, which is a frequency-modulated continuous wave (FMCW) MIMO radar system operating in the K-band. The Ancortek radar is currently the only K-band MIMO commercial radar system that offers customized antenna configurations. It is shown that this radar system encounters the problem of interference between the various subchannels. We propose an optimal approach to mitigate the problem of cross-channel interference by inducing a propagation delay in one of the channels and apply range gating. The measurement results prove the effectiveness of the proposed approach by demonstrating a complete elimination of the interference problem. The application of the proposed solution on Ancortek’s SDR-KIT 2400T2R4 allows resolving all subchannel links in a distributed MIMO configuration. This allows using MIMO RF sensing techniques to track a moving scatterer (target) regardless of its direction of motion.