Multiple-input Multiple-output Radar Research Articles

A Feasibility Study of Nearshore Bathymetry Estimation via Short-Range K-Band MIMO Radar

This paper provides an assessment of a 24 GHz multiple-input multiple-output radar as a remote sensing tool to retrieve bathymetric maps in coastal areas. The reconstruction procedure considered here exploits the dispersion relation and has been previously employed to elaborate the data acquired via X-band marine radar. The estimation capabilities of the sensor are investigated firstly on synthetic radar data. With this aim, case studies referring to sea waves interacting with a constant and a spatially varying bathymetry are both considered. Finally, the reconstruction procedure is tested by processing real data recorded at Bagnoli Bay, Naples, South Italy. The preliminary results shown here confirm the potential of the radar sensor as a tool for sea wave monitoring.

BER comparisons of MIMO radar with uniform linear arrays

A smart antenna, which consists of several antenna elements makes a gain at the desired direction, makes nulls at the direction of interferers. In this article, the bit error rate is measured in different directions and distances, and the method of the minimum variance is used with the concept of the link budget when the weights of the multiple input multiple output (MIMO) radar are calculated with the number of N receiver elements and with the number of M transmitter elements. The less the number of interferers is, or using MIMO, the better the system is.

Traffic Target Location Estimation Based on Tensor Decomposition in Intelligent Transportation System

As the safety problems and economic losses caused by traffic accidents are becoming more and more serious, intelligent transportation system (ITS) came into being. After the outbreak of COVID-19, how to achieve effective traffic scheduling and macro command under less contact has attracted more attention. Therefore, the location estimation of traffic objectives is a key issue. In the developed framework, for the target parameter estimation in traffic, frequency diversity array multiple-input multiple-output (FDA-MIMO) radar is introduced into ITS, and tensor decomposition is used to process transportation big data (TBD) to improve the real-time performance of target location estimation. Unfortunately, spatial colored noise and array gain-phase error will affect the performance of FDA-MIMO radar in ITS. An algorithm that can solve the angle-range estimation problem of FDA-MIMO radar in the co-existence of array gain-phase error and spatial colored noise is proposed. Firstly, the four-dimensional tensor is constructed by using the temporal un-correlation of colored noise. Therefore, the influence of colored noise in ITS is removed. Secondly, the direction matrix containing target information is obtained by parallel factor (PARAFAC) decomposition. For the array gain-phase error, the optimization problem is constructed, and the Lagrange multiplier is employed to calculate the optimal solution. The effect of gain-phase error is eliminated by utilizing the optimal solution and the direction matrices. Finally, the location information of motor vehicle is achieved by calculating the solution of least square (LS) fitting. The developed scheme can achieve the location information of motor vehicles in the co-existence of array gain-phase error and spatial colored noise. Comprehensive numerical experiments illustrate that the developed scheme in ITS can efficiently obtain the location information of motor vehicles.

RAU-Net-Based Imaging Method for Spatial-Variant Correction and Denoising in Multiple-Input Multiple-Output Radar

The conventional back projection (BP) algorithm is an accurate time-domain algorithm widely used for multiple-input multiple-output (MIMO) radar imaging, owing to its independence of antenna array configuration. The time-delay curve correction back projection (TCC-BP) algorithm greatly reduces the computational complexity of BP but suffers from spatial-variant correction, sidelobe interference and background noise due to the use of coherent superposition of echo time-delay curves. In this article, a residual attention U-Net-based (RAU-Net) MIMO radar imaging method that adapts complex noisy scenarios with spatial variation and sidelobe interference is proposed. On the basis of the U-Net underlying structure, we develop the RAU-Net with two modules: a residual unit with identity mapping and a dual attention module to obtain resolution spatial-variant correction and denoising on real-world MIMO radar images. The network realizes MIMO radar imaging based on the TCC-BP algorithm and substantially reduces the total computational time of the BP algorithm on the basis of improving the imaging resolution and denoising capability. Extensive experiments on the simulated and measured data demonstrate that the proposed method outperforms both the traditional methods and learning-imaging methods in terms of spatial-variant correction, denoising and computational complexity.

Bistatic MIMO radar height estimation method based on adaptive beam-space RML data fusion

This paper focuses on the beam-space target height estimation for bistatic multiple-input multiple-output (MIMO) radars, which is greatly affected by the multipath effect in low-elevation areas. The beam-space technique compresses the data and reduces computation, making it an ideal solution for this problem. However, there is a lack of research on beam-space target height estimation for bistatic MIMO radar, which this paper aims to address. In order to obtain the target height parameters accurately, we propose bistatic MIMO radar height estimation method based on adaptive beam-space refined maximum likelihood (RML) data fusion. First, we analyze and simplify the signal model, and obtain rough estimation of direction of departure (DOD) and direction of arrival (DOA) using digital beamforming (DBF) scanning technique; then, we convert target signals from the element space to the beam-space, separates the transmitter and the receiver signals, and obtain two target height estimations using the beam-space RML algorithm; finally, the minimum mean square error (MSE) criterion is used to fuse the two height estimations of the transmitter and the receiver. On this basis, we also analyze the application and advantages of RML algorithm in complex terrain through the measured data. In addition, the computational complexity of the proposed algorithm and the comparison algorithm is also given. Through some simulation results, it is not difficult to find that the proposed algorithm has good estimation accuracy and robustness.

Spectral estimation model for linear displacement and vibration monitoring with GBSAR system

In recent years, there has been a growing interest in the development of ground-based Synthetic Aperture Radars (GBSAR) for the purpose of monitoring structural displacements. GBSAR offers high-resolution monitoring over a wide area and can capture data every few minutes. However, compact high-frequency multiple input multiple output (MIMO) radars have emerged as an alternative for monitoring sub-second displacements, such as structural vibrations. MIMO radar has sub-second acquisition interval. However, it has limited cross-range resolution compared to GBSAR, and interference between antennas and presence of multiple scatterers in the scene can cause strong sidelobes in the processed data. On the other hand, GBSAR utilizes a long synthetic aperture to achieve high cross-range resolution. However, due to its longer data acquisition time compared to MIMO radar, conventional methods are insufficient for detecting scatterers’ sub-second displacements that occur during the data acquisition process. This study proposes a method to effectively monitor sub-second or sub-minute displacements using GBSAR signals. The proposed method enhances the conventional radar interferometric processes by employing spectral estimation, allowing for multi-dimensional detection of targets’ azimuth angle, linear displacement, and vibrational characteristics. Consequently, this method improves both the processing of MIMO radar data and enables high-resolution fast displacement monitoring from GBSAR signals. The paper presents the theoretical details and mathematical formulations of the proposed method for both MIMO radar and GBSAR imaging modes. To evaluate the effectiveness of the proposed method, numerical simulations and real experiments are conducted. The experimental results validate the capability of the proposed method in both GBSAR and MIMO configuration modes for high-resolution monitoring of fast linear displacements and vibrations. The results exhibit promising signal-to-noise ratio (SNR) and peak-to-sidelobe ratio (PSLR) values.

OFDM-MIMO radar assisted dual-function radar communication system using index modulation

Dual-function radar communication (DFRC) system exploits a hardware platform to achieve both radar and communication functions. Compared to utilizing separate radar and communication systems, DFRC has better performance in spectrum utilization. In this paper, assisted by orthogonal frequency division multiplexing-multiple input multiple output (OFDM-MIMO) radar, we propose a DFRC system architecture combining generalized space shift keying (GSSK) modulation and subcarrier index modulation (SIM) techniques. In this scheme, linear frequency modulation (LFM) signal is explored as the transmission waveform of OFDM-MIMO radar to complete the radar function. Furthermore, the proposed scheme combines GSSK and SIM to carry the communication information to the antenna index and subcarrier index to complete the communication function. Based on this architecture, a novel low complexity frequency detection communication demodulation algorithm is proposed, and the achievable rate communication performance of the system is analyzed. Numerical and simulation results verify the feasibility and effectiveness of the proposed system.

Robust moving target detection for FDA-MIMO radar with steering vector mismatches

This paper studies a robust moving target detection problem for frequency diverse array multiple-input multiple-output (FDA-MIMO) radar with a target embedded in Gaussian clutter with an unknown covariance matrix. We focus on the case of signal mismatch where the errors exist in the transmit-receive vector and Doppler steering vector. To solve the above detection problem, we adopt the subspace approach, where the true and nominal transmit-receive and Doppler steering vectors are assumed to lie in four-dimensional and two-dimensional subspaces, respectively, for the special framework for FDA-MIMO radar. At the detector design stage, we propose three robust adaptive detectors based on the one-step generalized likelihood ratio test (GLRT), two-step GLRT, and Wald test, respectively, with the training data free. The proposed detectors own the constant false alarm rate (CFAR) property against the clutter covariance matrix. The numerical examples validate the effectiveness of the proposed detectors.

Fast real-valued tensor decomposition framework for parameter estimation in FDA-MIMO radar

Frequency diversity array - Multiple input multiple output (FDA-MIMO) radar has a two-dimensional angle-range dependence due to the existence of certain frequency offset between transmitting elements. For obtaining angle-range estimation, a fast real-valued tensor propagator method (PM) for FDA-MIMO radar is developed. The developed approach is based on the real-valued tensor PM, which not only utilizes the original structural information of multidimensional information to improve estimation accuracy, but also eliminates the process of high-order singular value decomposition (HOSVD) on multidimensional data, greatly reducing computational complexity. Firstly, the unitary transformation technique is employed to convert the constructed tensor into a real-valued tensor. Next, construct a covariance tensor to obtain operator matrices in different directions. Then, a signal subspace is constructed using operator matrices. Finally, the selection matrices and the obtained signal subspace are employed to estimate angle and range information. The proposed algorithm can not only achieve parameter estimation at low snapshots, but also has much lower computational complexity than other algorithms at high snapshots. Therefore, the developed approach greatly reduces computational complexity while ensuring estimation accuracy, which enables it to be applied to massive FDA-MIMO radars. Simulation results confirm the accuracy advantage and high-efficiency of our algorithm.

A Novel MIMO Radar Orthogonal Waveform Design Algorithm Based on the Multi-Objective Improved Archimedes Optimization Algorithm

Realization and enhancement of detection techniques for multiple-input–multiple-output (MIMO) radar systems require polyphase code sequences with excellent orthogonality characteristics. Therefore, orthogonal waveform design is the key to realizing MIMO radar. Conventional orthogonal waveform design methods fail to ensure acceptable orthogonal characteristics by individually optimizing the autocorrelation sidelobe peak level and the cross-correlation sidelobe peak level. In this basis, the multi-objective Archimedes optimization algorithm (MOIAOA) is proposed for orthogonal waveform optimization while simultaneously minimizing the total autocorrelation sidelobe peak energy and total cross-correlation peak energy. A novel optimal individual selection method is proposed to select those individuals that best match the weight vectors and lead the evolution of these individuals to their respective neighborhoods. Then, new exploration and development phases are introduced to improve the algorithm’s ability to increase its convergence speed and accuracy. Subsequently, novel incentive functions are formulated based on distinct evolutionary phases, followed by the introduction of a novel environmental selection method aimed at comprehensively enhancing the algorithm’s convergence and distribution. Finally, a weight updating method based on the shape of the frontier surface is proposed to dynamically correct the shape of the overall frontier, further enhancing the overall distribution. The results of experiments on the orthogonal waveform design show that the multi-objective improved Archimedes optimization algorithm (MOIAOA) achieves superior orthogonality, yielding lower total autocorrelation sidelobe peak energy and total cross-correlation peak energy than three established methods.

Multi-scale moving target detection with FDA-MIMO radar

The recently developed frequency diverse array (FDA) radar applies a small frequency increment across the array elements, which provides advantages in clutter suppression, jamming suppression, and target detection for existing radar systems. In this paper, the frequency increment is enlarged to guarantee that the transmit waveforms are orthogonal in frequency domain for multiple-input multiple-output (MIMO) radar. A multi-scale moving target detection method of FDA-MIMO radar is proposed. The FDA radar with a large frequency increment suffers from the deteriorated decorrelation of scatterers within the same range bin and causes the inner-bin range dependence (IRD) property in transmit spatial domain. By taking additional space–time snapshots in transmit domain as training samples, the space–time adaptive processing (STAP) is applied in joint receive and Doppler domain for clutter suppression, which alleviates the requirement of insufficient training samples in non-stationary clutter environment. In the sequel, a set of beamformers within the whole angular domain of interest is utilized for target detection, which achieves target detection under the condition of random target locations and sizes. Numerical simulation results are provided to verify the effectiveness of the proposed method.

Efficient physics-based learned reconstruction methods for real-time 3D near-field MIMO radar imaging

Near-field multiple-input multiple-output (MIMO) radar imaging systems have recently gained significant attention. These systems generally reconstruct the three-dimensional (3D) complex-valued reflectivity distribution of the scene using sparse measurements. Consequently, imaging quality highly relies on the image reconstruction approach. Existing analytical reconstruction approaches suffer from either high computational cost or low image quality. In this paper, we develop novel non-iterative deep learning-based reconstruction methods for real-time near-field MIMO imaging. The goal is to achieve high image quality with low computational cost at compressive settings. The developed approaches have two stages. In the first approach, physics-based initial stage performs adjoint operation to back-project the measurements to the image-space, and deep neural network (DNN)-based second stage converts the 3D backprojected measurements to a magnitude-only reflectivity image. Since scene reflectivities often have random phase, DNN processes directly the magnitude of the adjoint result. As DNN, 3D U-Net is used to jointly exploit range and cross-range correlations. To comparatively evaluate the significance of exploiting physics in a learning-based approach, two additional approaches that replace the physics-based first stage with fully connected layers are also developed as purely learning-based methods. The performance is also analyzed by changing the DNN architecture for the second stage to include complex-valued processing (instead of magnitude-only processing), 2D convolution kernels (instead of 3D), and ResNet architecture (instead of U-Net). Moreover, we develop a synthesizer to generate large-scale dataset for training the neural networks with 3D extended targets. We illustrate the performance through experimental data and extensive simulations. The results show the effectiveness of the developed physics-based learned reconstruction approach compared to commonly used approaches in terms of both runtime and image quality at highly compressive settings. Our source codes and dataset are made available at https://github.com/METU-SPACE-Lab/Efficient-Learned-3D-Near-Field-MIMO-Imaging upon publication to advance research in this field.

Antenna Placement Optimization for Distributed MIMO Radar Based on a Reinforcement Learning Algorithm

This paper studies an optimization problem of antenna placement for multiple heading angles of the target in a distributed multiple-input multiple-output (MIMO) radar system. An improved method to calculate the system’s coverage area in light of the changing target heading is presented. The antenna placement optimization problem is mathematically modelled as a sequential decision problem for compatibility with reinforcement learning solutions. A reinforcement learning agent is established, which uses the long short-term memory (LSTM)-based proximal policy optimization (PPO) method as the core algorithm to solve the antenna placement problem. Finally, the experimental findings demonstrate that the method can enhance the coverage area of antenna placement and thus has reference value for providing new ideas for the antenna placement optimization of distributed MIMO radar.

Computationally efficient angle estimation of bistatic MIMO radar based on multimodal optimization

AbstractIn this letter, a computationally efficient multiple signal classification (MUSIC)‐based evolutionary algorithm for angle estimation of bistatic multiple‐input multiple‐output (MIMO) radar is proposed. The existing MUSIC algorithms require a computationally cumbersome two‐dimensional (2D) peak searching and the performance is highly related to the grid that set, which leads to a conflict between the computational efficiency and estimation performance. To address this difficulty, a multimodal quantum‐inspired salp swarm algorithm, integrating kmeans clustering technique, is proposed to substitute the 2D peak searching to obtain multiple maxima of the MUSIC algorithm. The resulting computationally efficient algorithm obviously reduces the computational complexity of the MUSIC algorithm, avoids grid errors, and further exploits the potential of the MUSIC algorithm. Numerical simulations in various scenarios are carried out to verify the superiority of the method.

A Method to Suppress Interferences Based on Secondary Compensation with QPC-FDA-MIMO Radar

The traditional phased array radar is unable to distinguish mainlobe deceptive interference from the true target when they share an identical angle. To address this issue, a method is developed to suppress the interference in the joint transmit–receive–Doppler frequency domain, using secondary compensation and quadratic phase code (QPC) with the frequency diversity array multiple-input multiple-output (FDA-MIMO) radar. The QPC technique is employed by encoding the transmitted pulses in various spatial channels, which extends the degrees-of-freedom (DOFs) of the system. To distinguish and suppress the false targets behind the true target by at least one pulse, Doppler compensation is performed and Doppler filters are employed. To distinguish the fast-generated false targets, which are located in the same pulse with the true target, the spatial frequency compensation is implemented, which equivalently relocates the mainlobe interference to the sidelobes. Via the singular spectrum analysis (SSA) technique and the data-dependent beamforming, the interferences within the ambiguity region are suppressed. Simulation results verify the effectiveness of the method.

A Space–Time–Range Joint Adaptive Focusing and Detection Method for Multiple Input Multiple Output Radar

The Multiple Input Multiple Output (MIMO) radar, as a new type of radar, emits orthogonal waveforms, which provide it with waveform diversity characteristics, leading to increased degrees of freedom and improved target detection performance. However, it also poses challenges such as difficulty in meeting higher data demand, separating waveforms, and suppressing the multidimensional sidelobes (range sidelobes, Doppler sidelobes, and angle sidelobes) of targets. Phase-coded signals are frequently employed as orthogonal transmission signals in the MIMO radar. However, these signals exhibit poor Doppler sensitivity, and the intra-pulse Doppler frequency shift can have an impact on the effectiveness of the matching filtering process. To address the aforementioned concerns, this paper presents a novel approach called the Space–Time–Range Joint Adaptive Focusing and Detection (STRJAFD) method. The proposed method utilizes the Mean Square Error (MSE) criterion and integrates spatial, temporal, and waveform dimensions to achieve efficient adaptive focusing and detection of targets. The experimental results demonstrate that the proposed method outperforms conventional cascaded adaptive methods in effectively addressing the matching mismatch issue caused by Doppler frequency shift, achieving super-resolution focusing, possessing better suppression effects on three-dimensional sidelobes and clutter, and exhibiting better detection performance in low signal-to-clutter ratio and low signal-to-noise ratio environments. Furthermore, STRJAFD is unaffected by coherent sources and demands less data.

Pulse train coding and decoding matrix design based ECCM scheme for MIMO radar against interrupted sampling repeater jamming

In this paper, we propose a pulse train coding and decoding matrix design based electronic counter-countermeasure (ECCM) scheme for multiple-input multiple-output (MIMO) radar against interrupted sampling repeater jamming (ISRJ). The ECCM scheme includes two steps: jamming pulse compression (PC) sidelobes suppression and jamming peaks identification. By joint design of coding and decoding matrix, the proposed ECCM scheme can simultaneously suppress PC sidelobes of target echoes and jamming signals. Compared with the existing methods, the proposed scheme can achieve the improvement in jamming suppression and target detection without signal-to-noise ratio loss, and the coding and decoding matrix design can be realized offline. Besides, the phase-only coding matrix can be directly applied to the MIMO radar with multi-beam transmit beampattern. After sidelobes suppression, an identification matrix is proposed to identify the jamming peaks. In specifical situation, the identification process does not require ISRJ parameter, and the ISRJ parameter estimation can be converted into a convex optimization problem in other situations. At last, simulated results corroborate the effectiveness of the anti-ISRJ performance with the proposed ECCM scheme.

Aperture Synthesis Imaging of Ionospheric Irregularities Using Time Diversity MIMO Radar

AbstractAperture‐synthesis images of ionospheric irregularities in the equatorial electrojet are computed using multiple‐input multiple‐output (MIMO) radar methods at the Jicamarca Radio Observatory. MIMO methods increase the number of distinct interferometry baselines available for imaging (by a factor of essentially three in these experiments) as well as the overall size of the synthetic aperture. The particular method employed here involves time‐division multiplexing or time diversity to distinguish pulses transmitted from different quarters of the Jicamarca array. The method comes at the cost of a large increase in computation time and complexity and a reduced signal‐to‐noise ratio. We discuss the details involved in the signal processing and the trade space involved in image optimization.

Frequency Increment Design Method of MR-FDA-MIMO Radar for Interference Suppression

In the present complex electromagnetic environment, radar target detection is threatened by different kinds of interferences, especially mainlobe deceptive interference, which occupies the same energy distributions of targets spatially, meaning that targets and interferences cannot be discriminated. To make matters worse, the number of suppressible interferences is limited by the number of physical array elements, leading to the degradation of the suppression performance of traditional radar. In this work, we propose a frequency-increment-based interference suppression method for minimum redundancy frequency diverse array multiple-input multiple-output (MR-FDA-MIMO) radar, which effectively solves the aforementioned two problems. The interference suppression method consists of two steps: (i) in the sidelobe barrage interference suppression stage, the interference-plus-noise covariance matrix is reconstructed to overcome the influence of the true targets and mainlobe deceptive interference on the performance of the beamformer; (ii) in the mainlobe deceptive interference suppression stage, a nonadaptive beamforming method is employed to suppress mainlobe deceptive interference and overcome the impact of insufficient virtual samples on interference suppression performance. Additionally, we design a frequency-increment-based MR-FDA-MIMO radar, fully utilizing the advantages of the virtual array to enhance interference suppression performance and increase the number of interferences. Numerical experiments undertaken demonstrate the effectiveness of the algorithm under different scenarios.

Performance Evaluation of Photonics-Based Coherent MIMO Radar Systems for Maritime Surveillance

Multiple-input multiple-output (MIMO) radars offer significant advantages over conventional standalone radars in terms of target detection and localization capabilities. However, to fully exploit their potential, such systems require excellent time and phase synchronization among the central unit and the radar heads. Only recently, thanks to microwave photonics (MWP) techniques, it has been possible to develop the first coherent MIMO radar demonstrators. In this paper, a simulation tool in MATLAB programming language is proposed to model coherent MIMO radars based on MWP techniques in maritime surveillance applications. Moreover, a novel approach for estimating the radar cross section of extended maritime targets is presented. The system performance in co-located and distributed MIMO configuration, as well as in single- and multi-band operations, is evaluated by means of relevant key performance indicators (KPIs). Simulations are carried out in two close-to-reality scenarios. In the first, a co-located MIMO radar is mounted on top of a patrolling vessel. In the second, a distributed MIMO radar is deployed inside a port. The proposed KPIs are analyzed in terms of both geometric and frequency diversities of the system, laying the foundations for general system optimization criteria valid in any given surveillance application.