Multiple-input Multiple-output Radar Research Articles

Phase Error Correction in Sparse Linear MIMO Radar Based on the Equivalent Phase Center Principle

Multiple-input multiple-output (MIMO) technology is widely used in the field of radar imaging. Array sparse optimization reduces the hardware cost of MIMO radar, while virtual aperture and the equivalent phase center (EPC) principle simplify the radar signal model and reduce the computation and complexity of imaging algorithms. However, the application of sparse array structure and the EPC principle produces a non-negligible phase error, which affects the imaging quality. This paper simplifies the MIMO radar signal model based on the phase center approximation, analyzes the phase error generated by this method, and proposes an improved phase error correction method to solve the problem that the target cannot be well-focused at non-reference distance during imaging. In addition, this paper designs a sparse linear MIMO array with a periodic structure, which reduces the number of transmitting and receiving units, system complexity, and hardware costs. The proposed phase correction method was combined with the wavenumber domain algorithm to simulate and experiment on the designed antenna array, and good experimental results were obtained to verify the effectiveness of the proposed method.

Combinatorial-restless-bandit-based transmitter–receiver online selection of distributed MIMO radar with non-stationary channels

In a distributed multiple-input multiple-output (MIMO) radar for tracking moving targets, optimizing sensible selections of the transmitter–receiver pairs is crucial for maximizing the sum of signal-to-interference-plus-noise ratios (SINRs), as it directly affects the tracking accuracy. In solving the trade-off between exploitation and exploration in non-stationary channels, the optimization problem is modeled by a restless multi-armed bandits model. This paper regards the estimated SINR mean reward as the state of an arm (transceiver channel). The SINR reward of each arm is estimated based on whether it is probed. A closed loop is established between SINR rewards and the dynamic states of targets, which are estimated via the interacting multiple model-unscented Kalman filter. The combinatorial optimized selection of transmitter–receiver pairs at each time is accomplished by using the binary particle swarm optimization with the SINR index fitness function, where the index represents the upper bound on the confidence of the SINR reward. Above all, a multi-group combinatorial-restless-bandit closed-loop (MG-CRB-CL) algorithm is proposed. Simulation results for different scenarios are provided to verify the effectiveness and superior performance of MG-CRB-CL.

Incoherent Detection Performance Analysis of the Distributed Multiple-Input Multiple-Output Radar for Rice Fluctuating Targets

Utilizing spatial diversity, the distributed multiple-input multiple-output (MIMO) radar has the potential advantage of improving system detection performance. In this paper, the incoherent detection performance of distributed multiple-input multiple-output (MIMO) radars is investigated for Rice fluctuating targets. To calculate the incoherent detection probability, the moment generating function (MGF) of the Rice variable is expanded as the infinite series form. By inverting the product of MGFs of multiple independent Rice variables, new closed-form expressions for the probability density function (PDF) of the sum of independent and weighted squares of Rice variables are proposed. The proposed PDF expression for the sum of independent, non-identically distributed (i.n.i.d.) Rice variables involves an infinite series in terms of the confluent Lauricella function. Specially, the PDF for the sum of independent identically distributed (i.i.d.) Rice is expressed as the confluent hypergeometric function-based infinite series. In addition, the uniform convergence of the proposed PDF expression is also validated. Using this proposed expression, the closed-form and approximate expressions of the incoherent detection probability of MIMO radar are derived, respectively. Numerically evaluated results are illustrated and compared with Monte Carlo (MC) simulations to validate the accuracy of the derivations.

Orthogonal Waveform Design for MIMO Radar Using Deep Learning

Abstract This paper explores the design of orthogonal waveforms for multiple-input multiple-output (MIMO) radar. We combine the peak sidelobe level (PSL) with the omnidirectional beampattern matching as the optimization objective. To address this challenging non-linear and non-convex problem, we propose an unsupervised optimization network based on residual network and attention mechanism. This approach harnesses the potent nonlinear fitting capabilities of neural networks to address the complex optimization problem. Simulation results indicate that the waveforms designed by the algorithm proposed in this paper exhibit better orthogonality than those designed by the traditional algorithm.

A Novel Waveform Optimization Method for Orthogonal-Frequency Multiple-Input Multiple-Output Radar Based on Dual-Channel Neural Networks.

The orthogonal frequency-division multiplexing (OFDM) mode with a linear frequency modulation (LFM) signal as the baseband waveform has been widely studied and applied in multiple-input multiple-output (MIMO) radar systems. However, its high sidelobe levels after pulse compression affect the target detection of radar systems. For this paper, theoretical analysis was performed, to investigate the causes of high sidelobe levels in OFDM-LFM waveforms, and a novel waveform optimization design method based on deep neural networks is proposed. This method utilizes the classic ResNeXt network to construct dual-channel neural networks, and a new loss function is employed to design the phase and bandwidth of the OFDM-LFM waveforms. Meanwhile, the optimization factor is exploited, to address the optimization problem of the peak sidelobe levels (PSLs) and integral sidelobe levels (ISLs). Our numerical results verified the correctness of the theoretical analysis and the effectiveness of the proposed method. The designed OFDM-LFM waveforms exhibited outstanding performance in pulse compression and improved the detection performance of the radar.

Antenna resource allocation for netted radar system with main-lobe deceptive jamming suppression

An adaptive beamforming method based on the polarimetric scattering matrix (PSM) of targets to suppress deceptive jamming located in the beam pattern main lobe is proposed in this study. For the netted radar system, the true/false mixed targets of different/same range ambiguity region are distinguished by the range-PSM dimension; the flexible allocation of antenna resource is realised by the number of estimated targets; the main-lobe deceptive jamming is also suppressed by the adaptive beamforming method. First, a polarimetric multiple-input multiple-output (MIMO) radar with a frequency diverse array (FDA) was considered as the initial antenna configuration. Subsequently, the performance of the transmit–receive spatial frequency was analysed, and the characteristics of false targets in the polarimetric FDA-MIMO radar were discussed. We then considered the optimisation problem between the transmit polarisation and the frequency increment, as well as the allocation of antenna resources. Finally, an adaptive beamforming method was utilised to achieve deceptive jamming suppression in the main lobe. The effectiveness of PSM was verified with respect to identifying true/false targets, and the jamming suppression performance was evaluated in the main lobe by comparing it with existing systems via simulation results.

A novel BP algorithm‐based UWB MIMO ground‐penetrating imaging system

AbstractUltra‐wideband (UWB) multiple input multiple output (MIMO) radar has been widely used in detection systems due to its advantages, such as low cost and high data acquisition capability. The detection system is applied in various ground‐penetrating applications, including mine detection and earthquake rescue. In this paper, a novel UWB MIMO radar system based on step‐frequency continuous wave is designed for underground target detection. To improve the penetration depth and high‐range resolution, a new miniature Vivaldi antenna with a bandwidth of 1–3 GHz is designed and integrated into the ground‐penetrating imaging system. Furthermore, a multiscale weighted time‐domain back projection (BP) algorithm is proposed to suppress artifacts and reduce the computational complexity of the BP algorithm. The designed system is tested by detecting stationary targets. The experimental results agree well with the simulation. The reconstructed images show that the designed MIMO radar system can successfully image underground targets.

Sparse decoupling imaging network for wideband two‐dimensional MIMO radar imaging using model‐assisted and attention mechanism

AbstractThe problem of sparse decoupling radar imaging methods based on deep learning is researched. An improved model‐driven learning imaging network with a complex‐valued convolution block attention module plugged into each sub‐network is proposed. This method can solve the high sidelobe and coupling problem in sparse wideband Multiple‐Input Multiple‐Output (MIMO) radar. In addition, it can better focus on the target area and capture target information to boost model representation power. Experimental results verify the validity of the proposed method.

Transmit beampattern design for FDA-MIMO radar using complex manifold optimization

Frequency diverse array multiple-input multiple-output (FDA-MIMO) radar can produce time-stationary range-angle dependent beampattern. The problem with these beampatterns is the repeating lobes in the angle-range domain. Different methods are proposed to obtain better beampattern in terms of grating lobe and sidelobe suppression by using frequency offsets. However, these methods mostly choose the frequency offsets using an analytic expression and do not offer a control on the resulting range beampattern. In this paper, a new method for transmit beampattern design in range is presented. This method transfers the design problem into a tangential space and solves the problem using complex manifolds through an optimization procedure. The proposed approach considers amplitude tapering in the transmitter antenna elements and the practical instrumented range of the radar. Range beampattern constraints can be defined conveniently leading to an effective design process. Several simulations are done to show that the proposed method can significantly improve the beamwidth and sidelobe level in range patterns.

Minimum peak sidelobe ratio filter for MIMO radar via convex optimization

Multiple input multiple output (MIMO) radar retains transmitting freedom by transmitting multiple waveforms simultaneously. However, due to the cross-correlation between waveforms, the performance of range sidelobes is degraded. So, low sidelobes techniques are especially demanded in MIMO radar. This article proposes a mismatched filtering method for minimizing MIMO radar’s peak sidelobe level ratio (PSLR). In this method, we formulate the problem as a convex optimization (CO) problem and thus ensure its solution is globally optimal. To apply mismatched filters to MIMO radar, we introduce the traditional strategy and propose a new one. Instead of attempting to minimize all auto-correlations and cross-correlations of all waveforms as in the traditional strategy, our proposed strategy directly optimizes the mismatched filter of the synthesis signal for each considered direction. To verify the effectiveness of our method, numerical simulations are carried out. Firstly, the PSLR effects of those two strategies are compared. The results indicate that our proposed strategy dramatically improves the PSLR performance of MIMO radar compared to the traditional one. Then, the influences of several parameters on the PLSR performance are revealed, which indicates that our method is flexible enough to make trade-offs between PSLR and other parameters.

Spatial–Temporal Joint Design and Optimization of Phase-Coded Waveform for MIMO Radar

By simultaneously transmitting multiple different waveform signals, a multiple-input multiple-output (MIMO) radar possesses higher degrees of freedom and potential in many aspects compared to a traditional phased-array radar. The spatial–temporal characteristics of waveforms are the key to determining their performance. In this paper, a transmitting waveform design method based on spatial–temporal joint (STJ) optimization for a MIMO radar is proposed, where waveforms are designed not only for beam-pattern matching (BPM) but also for minimizing the autocorrelation sidelobes (ACSLs) of the spatial synthesis signals (SSSs) in the directions of interest. Firstly, the STJ model is established, where the two-step strategy and least squares method are utilized for BPM, and the L2p-Norm of the ACSL is constructed as the criterion for temporal characteristics optimization. Secondly, by transforming it into an unconstrained optimization problem about the waveform phase and using the gradient descent (GD) algorithm, the hard, non-convex, high-dimensional, nonlinear optimization problem is solved efficiently. Finally, the method’s effectiveness is verified through numerical simulation. The results show that our method is suitable for both orthogonal and partial-correlation MIMO waveform designs and efficiently achieves better spatial–temporal characteristic performances simultaneously in comparison with existing methods.

Joint Antenna Scheduling and Power Allocation for Multi-Target Tracking under Range Deception Jamming in Distributed MIMO Radar System

The proliferation of electronic countermeasure (ECM) technology has presented military radar with unprecedented challenges as it remains the primary method of battlefield situational awareness. In this paper, a joint antenna scheduling and power allocation (JASPA) scheme is put forward for multi-target tracking (MTT) in the distributed multiple-input multiple-output (D-MIMO) radar. Aiming at radar resource scheduling in the presence of range deception jamming (RDJ), the false target discriminator is designed based on the Cramer–Rao lower bound (CRLB) in terms of the spoofing range, and the predicted conditional CRLB (PC-CRLB) plays a role in evaluating tracking accuracy. The JASPA scheme integrates the quality of service (QoS) principle to develop an optimization model based on false target discrimination, with the objective of enhancing both the discrimination probability of false targets and the tracking accuracy of real targets concurrently. Since the optimal variables can be separated in constraints, a four-step optimization cycle (FSOC)-based algorithm is developed to solve the multidimensional non-convex problem. Numerical simulation results are provided to illustrate the effectiveness of the proposed JASPA scheme in dealing with MTT in the RDJ environment.

Diversity-multiplexing tradeoff for cooperative MIMO radar and communications system

In this paper, multiple-input multiple-output (MIMO) integrated radar and communications (IRC) system is studied, where the radar and communications parts work cooperatively, leading to a hybrid active-passive (HAP) MIMO radar and a radar-aided (RA) MIMO communications subsystems. To unify the performance metric for both subsystems, the diversity gain and multiplexing gain are derived for the RA MIMO communications and the HAP MIMO radar subsystems, based on which the tradeoff between the diversity and multiplexing gains is analyzed theoretically for each of the subsystems. The obtained diversity gain and multiplexing gain can facilitate the design and optimization of the IRC system. Employing these two unified performance metrics, the IRC system antenna number assignment is formulated as a multi-objective optimization problem, and the Pareto solutions are provided. The correctness of theoretical analysis is verified by simulations. Our results can provide useful guidance for system designers.

An ADMM approach for elliptic positioning in non-line-of-sight environments

Elliptic positioning (EP) has recently emerged as a prevailing subject within localization research, holding significant relevance for a variety of multistatic systems including distributed multiple-input multiple-output radar, sonar, and wireless sensor networks. Mathematically, EP refers to estimating the location of a signal-reflecting/relaying target from the bistatic range (BR) measurements acquired by employing multiple spatially separated transmitters and receivers. BRs, as a specific type of range-based sensor observations, will however be positively biased when non-line-of-sight (NLOS) signal propagation occurs. Such a phenomenon is widespread across various localization scenarios, which can seriously degrade the positioning accuracy if not properly addressed. Through the alternating direction method of multipliers, this contribution introduces a computationally efficient iterative algorithm designed for error-mitigated EP in NLOS environments. In addition to target position coordinates, we incorporate a non-negatively bounded balancing parameter into the formulation and perform joint estimation, thereby achieving NLOS bias error reduction in a simple yet impactful manner. Numerical simulations are conducted to validate the functionality of the presented EP approach in NLOS situations.

High-resolution 2D-DoA sequential algorithm of azimuth and elevation estimation in automotive distributed system of coherent MIMO radars

Objectives. One of the main tasks of radiolocation involves the problem of increasing spatial resolution of the targets in the case of limited aperture of the radar antenna array and short length of time samples (snapshots). Algorithms must be developed to provide high angular resolution and low computational complexity. In order to conform with the existing Advanced Driver Assistance Systems requirements, modern cars are equipped with more than one radar having a common signal processing scheme to improve performance during target detection, positioning, and recognition as compared to a single radar. The present study aims to develop a two-dimensional Direction-of-Arrival algorithm with low computation complexity as part of distributed coherent automotive radar system for cases involving short time samples (snapshots).Methods. A virtual antenna array formation algorithm is formulated according to the two-dimensional Capon method. A proposed modification of two-dimensional Capon algorithm is based on sequentially estimating the directions of arrival for the distributed radar system. The Monte Carlo method is used to compare the effectiveness of the considered algorithms.Results. The 2D-DoA sequential algorithm of azimuth and elevation estimation is proposed. The comparative analysis results for the developed algorithm and classical 2D Capon method based on numerical simulation using Monte Carlo method are presented. The proposed scheme of DoA estimation for coherent signal processing of distributed radars is shown to lead to an improvement of the main considered metrics representing the probability of correctly estimating the number of targets, mean square error, and square error compared to a single radar system. The proposed low-computational algorithm shows the gain in complexity compared to full 2D Capon algorithm.Conclusions. The proposed two-stage algorithm for estimating the directions of arrival of signals in azimuth and elevation planes can be applied to the distributed system of coherent radars with several receiving and transmitting antennas representing multiple input multiple output (MIMO) radars. The algorithm is based on sequentially estimating the directions of arrival, implying estimation in the azimuthal plane at the first stage and estimation in the vertical plane at the second stage. The performance of a coherent radar system with limited antenna array configuration of separate radar is close in characteristics to a high-performance 4D-radar with a large antenna array system.

A novel geometric-based bistatic range grouping algorithm for multi-target localization in distributed MIMO radar systems

In this paper, a novel bistatic range grouping algorithm is proposed in order to localize multiple targets in distributed multiple-input multiple-output (MIMO) radar systems. In a distributed MIMO radar, target localization is performed based on bistatic ranges, the distances between antennas through a target, without directional information, and grouping bistatic ranges with respect to the target is a crucial step to estimate multiple targets efficiently. In contrast to the existing grouping schemes that use tentative target estimation or complicated joint bistatic range grouping and target estimation, the proposed algorithm uses only the geometric property of bistatic ranges, resulting in computationally efficient and robust grouping algorithm without noise enhancement, especially for realistic noisy environments where other existing grouping algorithms often fail. The performance of the proposed scheme is confirmed by evaluating the probability of miss detection and the probability of false alarm through numerical simulations for various environments including indirect paths and blocked paths. The root mean square error performance of the multi-target localization using the proposed bistatic grouping algorithm is also presented for realistic noisy environments.

Gait‐based human recognition based on millimetre wave multiple input multiple output radar point cloud constructed using velocity‐depth‐time

AbstractGait recognition is to recognise different individuals based on their faint differences of gait characteristics, which is different from and more challengeable than the recognition of human activities based on relatively bigger differences between different motions. Existing millimetre‐wave Multiple Input Multiple Output radar point cloud data contains time‐varying three‐dimensional spatial positions, velocity, and intensity information. How to enhance the accuracy of gait recognition by effectively utilising the available radar point cloud data has become an attractive research topic in recent years. A velocity‐depth‐time (VDT) based point cloud construction method for millimetre‐wave Multiple Input Multiple Output radar is proposed for gait recognition application, which can not only alleviate the sparsity problem of mmWave point cloud but also make the constructed point cloud to exhibit temporal structural features of micro‐motions, and therefore enable the successful application of PointNet++ to mmWave‐MIMO point cloud gait recognition. New point clouds are constructed by the proposed method using public gait recognition datasets of 10 and 20 individuals from mmWave‐MIMO radar, which are used to conduct gait recognition experiments using PointNet++. The results show that the point clouds constructed based on VDT are more conducive to the gait recognition task. Even using the classic PointNet++ model, which is not specially designed for radar point clouds, high recognition accuracy can be achieved for gait recognition tasks. The recognition accuracies are improved by 11% and 12% in this work for datasets of 10 and 20 individuals, respectively, compared with the 84% and 80% achieved by the traditional method using the same dataset and the same PointNet++ model, while the accuracies are improved by 5% and 12%, respectively, compared with the 90% and 80% achieved by the original dataset thesis method corresponding to 10‐individual and 20‐individual datasets.

Adaptive target detection for an FDA-MIMO radar in a mainlobe deceptive jamming and a partially homogeneous noise

In this paper, we investigate the adaptive target detection problem for a collocated frequency diverse array multiple-input multiple-output (FDA-MIMO) radar in the presence of mainlobe deceptive jamming and partially homogeneous noise with an unknown covariance matrix and scaling factor. Precisely, we establish a received signal model that includes the true target and false counterparts echoes in the presence of Gaussian noise (it includes thermal noise, interference suppression, and clutter after range compensation.). Furthermore, we consider that the range information of the true target and mainlobe deceptive targets is not identical. Therefore, we project the corresponding transmit steering vectors onto different subspaces with known subspace matrices but unknown coordinates. On the other hand, the true and false targets' receive steering vectors are considered to be from the same subspace. Finally, following the generalized likelihood ratio test (GLRT), the Rao and the Wald criteria, we design three adaptive detectors with two-step criteria, i.e., TGLRT, TRao, and TWald. We note that the proposed detectors maintain a constant false alarm rate (CFAR) against the scaling factor and the noise covariance matrix. The extensive numerical simulation results have validated the effectiveness of the proposed detectors.

Reduced-complexity multiple parameters estimation via toeplitz matrix triple iteration reconstruction with bistatic MIMO radar

In this advanced exploration, we focus on multiple parameters estimation in bistatic Multiple-Input Multiple-Output (MIMO) radar systems, a crucial technique for target localization and imaging. Our research innovatively addresses the joint estimation of the Direction of Departure (DOD), Direction of Arrival (DOA), and Doppler frequency for incoherent targets. We propose a novel approach that significantly reduces computational complexity by utilizing the Temporal-Spatial Nested Sampling Model (TSNSM). Our methodology begins with a multi-linear mapping mechanism to efficiently eliminate unnecessary virtual Degrees of Freedom (DOFs) and reorganize the remaining ones. We then employ the Toeplitz matrix triple iteration reconstruction method, surpassing the traditional Temporal-Spatial Smoothing Window (TSSW) approach, to mitigate the single snapshot effect and reduce computational demands. We further refine the high-dimensional ESPRIT algorithm for joint estimation of DOD, DOA, and Doppler frequency, eliminating the need for additional parameter pairing. Moreover, we meticulously derive the Cramér-Rao Bound (CRB) for the TSNSM. This signal model allows for a second expansion of DOFs in time and space domains, achieving high precision in target angle and Doppler frequency estimation with low computational complexity. Our adaptable algorithm is validated through simulations and is suitable for sparse array MIMO radars with various structures, ensuring higher precision in parameter estimation with less complexity burden.

Reinforcement Learning for FDA-MIMO Radar Power Allocation in Congested Spectral Environments

This paper studies the coexistence problem of frequency diverse array multiple-input multiple-output (FDA-MIMO) radar and communication systems. The communication system and radar share the same frequency band, and the overlap of the frequency spectrum will interfere with each other's performance, which constitutes a dynamic non-cooperative coexistence problem. We model the FDA-MIMO radar interference suppression process as a Markov decision process (MDP). The interference signals received by FDA-MIMO radar are generated by the communication system, including constant interference, frequency-hopping interference, and intermittent interference. FDA-MIMO radar realizes flexible transmission waveform spectrum control through transmission power allocation. Reinforcement learning (RL) is applied to obtain the optimal power allocation strategy of FDA-MIMO radar, and it is compared with the Sense and Avoid (SSA) algorithm. The simulation results show that the FDA-MIMO radar power allocation method based on reinforcement learning can effectively suppress the communication system's interference and improve the radar's signal-to-interference-and-noise ratio (SINR) and range resolution.