MIMO Radar System Research Articles

Joint Beamforming Design for Dual-Functional MIMO Radar and Communication Systems Guaranteeing Physical Layer Security

The dual-functional radar and communication (DFRC) technique constitutes a promising next-generation wireless solution, due to its benefits in terms of power consumption, physical hardware, and spectrum exploitation. In this paper, we propose sophisticated beamforming designs for multi-user DFRC systems by additionally taking the physical layer security (PLS) into account. We show that appropriately designed radar waveforms can also act as the traditional artificial noise conceived for drowning out the eavesdropping channel and for attaining increased design degrees of freedom (DoF). The joint beamforming design is formulated as a non-convex optimization problem for striking a compelling trade-off amongst the conflicting design objectives of radar transmit beampattern, communication quality of service (QoS), and the PLS level. Then, we propose a semidefinite relaxation (SDR)-based algorithm and a reduced-complexity version to tackle the non-convexity, where the globally optimal solutions are found. Moreover, a robust beamforming method is also developed for considering realistic imperfect channel state information (CSI) knowledge. Finally, simulation results are provided for corroborating our theoretical results and show the proposed methods’ superiority.

Enumeration PCRLB-Based Power Allocation for Multitarget Tracking With Colocated MIMO Radar Systems in Clutter

An effective resource allocation strategy can maximize the remote sensing performance of radar systems, such as target detection and tracking. In this paper, two typical power allocation (PA) strategies are developed for the multi-target tracking (MTT) task in colocated MIMO (C-MIMO) radar systems with the consideration of the clutter. The multi-beam concept and the posterior PDF fusion are adopted by the C-MIMO radar system to obtain the global posterior distribution. Specifically, each radar generates multiple simultaneous beams with controllable power during each interval. To ensure that the limited system resources can be utilized effectively, the online PA scheme is implemented according to the prior knowledge predicted from the tracking cyclic recursive feedback results. The posterior Cramér-Rao lower bound (PCRLB) is derived by enumerating all possible target detection and false alarm occurrence cases, and is utilized as the tracking performance metric since it provides a more accurate lower bound on the target state estimation in clutter. Besides, to solve the computationally expensive problem of this PCRLB caused by enumeration operation, we propose a two-step approximate approach. Then, combined with the system resource configuration, two different types of resource optimization problems are designed, namely, performance maximization for a fixed power budget and direct resource minimization. These formulated PA problems are shown to be non-convex and non-linear. Therefore, we further propose a modified particle swarm optimization (MPSO) algorithm to solve these problems efficiently. Simulation results verify the superiority and effectiveness of the proposed PA strategies in terms of tracking performance in clutter.

LPI-Based Transmit Resource Scheduling for Target Tracking with Distributed MIMO Radar Systems

This paper explores the effect of the low probability of intercept (LPI) performance optimization on target tracking in distributed MIMO (D-MIMO) radar systems, where an intercept receiver is equipped on the target. The aim of the proposed LPI-based transmit resource scheduling (LPI-TRS) strategy is to optimize the LPI performance of the D-MIMO systems meanwhile satisfy the target tracking accuracy requirement by the proper transmit resource management design. Different from the existing research, we introduce a cascading mathematical model to describe the intercept process with the specific intercept receiver. A cascading product of the probabilities of interception and detection deduces the probability of report <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ P_{R}$</tex-math></inline-formula> for the interceptor, to evaluate the LPI performance of one transmit radar node. Accordingly, the maximum <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ P_{R}$</tex-math></inline-formula> is employed, for the overall D-MIMO radar systems, to perform as the global LPI performance metric. Based on the min-max criterion, by minimizing the global LPI metric, a novel non-convex optimization problem is formulated wherein the requirements for target tracking performance and the system resource limitation are considered as the constraints. Wherein the tracking task is fulfilled by the two-tier hybrid processing framework. Meanwhile, we introduce a convex relaxation-based approach, so the sub-optimal results are acquired via a two-step fast computational search algorithm. Subsequently, the closed-loop scheme is set up to achieve LPI-based resource awareness in D-MIMO radar systems. Several numerical results demonstrate the theoretical analysis and show that the proposed LPI-TRS leads to significant LPI performance improvements.

Moving Target Detection Using a Distributed MIMO Radar System With Synchronization Errors

Moving Target Detection Using a Distributed MIMO Radar System With Synchronization Errors

Direct Target Localization With Quantized Measurements in Noncoherent Distributed MIMO Radar Systems

In this paper, a direct target localization algorithm with quantized measurements for non-coherent MIMO systems is proposed. In this system, each receiver transmits a low-bit quantized version of the echo rather than the full raw data to the fusion center. We construct a joint likelihood function based on the low-bit data of each receiver, from which the unknown target position can be directly determined. The Cramer-Rao lower bound (CRLB) is derived to analyze the localization performance of our proposed algorithm. To maximize the localization performance, a CRLB-based objective function is designed to obtain the optimum quantization thresholds. The formulated problem is a high-dimensional and non-convex optimization problem that when solved, determines two types of coupled parameters for the quantization thresholds and the complex-valued scaling coefficients of the signal. We propose a batch gradient descent embedded particle swarm optimization algorithm to solve this problem effectively. Numerical results show that the proposed algorithm delivers superior performance in terms of maximizing the overall localization performance, and the 3-bit quantized algorithm is able to provide performance that is very close to the unquantized algorithm. Experimental data recorded by three small radars are also provided to demonstrate the effectiveness of the proposed algorithm.

Joint antenna selection and waveform design for coexistence of MIMO radar and communications

As the problem of spectral congestion is becoming severe, the coexistence between two primary spectrum users, radar and communications, has spurred extensive research interest. To reduce the mutual interferences between the two functions, MIMO radar waveform design needs to consider the compatibility in both spectral and spatial domains, where the former is achieved by null forming in the frequency domain and the latter is achieved by shaped beampattern synthesis. Additionally, high power efficiency and low system overhead are two desirable characteristics for MIMO radar system design. To this end, we first introduce a new realistic waveform constraint, peak-to-valley-power-ratio (PVPR) constraint per antenna to improve the power efficiency. Then, combined with PVPR constraint, we propose a switchable individual antenna power control scheme to jointly optimize waveforms and antenna locations. We adopt a max–min beampattern matching criterion and impose the ell _{2,1}-norm penalty on the waveform matrix to promote the sparsity of the array. To solve the resultant non-smooth and non-convex problem, we develop a modified alternating directions method of multipliers, where a surrogate subproblem over primal variables is solved instead of the original problem, and its local convergence is analyzed. Finally, numerical experiments demonstrate the effectiveness and superiority of the proposed method over counterparts, especially obtaining the lowest sidelobe level and deeper spectral nulls using much fewer antennas.

In-Cabin MIMO Radar System for Human Dysfunctional Breathing Detection

The integration of wireless sensors into the automotive industry is making vehicles increasingly safe and autonomous. At present, vehicles are equipped with external sensors that facilitate maneuverability and braking and internal sensors that detect presence, seat belt state, and occupant vital signs. Early detection of dysfunctional breathing patterns can reduce the risk of accidents caused by drowsiness or fatigue. In this regard, this work presents an MIMO FMCW Radar System able to distinguish dysfunctional breathing patterns from human torso motion. The torso has been divided into six regions, and the amplitude of the elongation and the frequency of motion of each region have been measured during breathing. An elongation map is then constructed in order to graphically show the dysfunctional breathing patterns. The signal processing includes a peak search algorithm to detect elongation amplitude and a bandpass frequency filter to minimize dc components, random driver motion, and vehicle vibration. A torso elongation emulator phantom has been assembled with materials of skin-like relative permittivity and dc motors for calibration and validation of basic breathing patterns. Finally, the signal of the system is compared with that of a commercial respiration belt, and then the system is tested by performing measurements on people. The MIMO radar system is able to measure, differentiate, and classify patterns associated with dysfunctional breathing such as hyperventilation syndrome and thoracic dominant breathing.

Mimo technology in multi-radar systems for detecting stealthy air objects

The main trends in the development of air objects are analyzed (the means of air attack in the conduct of the Russian-Ukrainian war are taken as a basis). It has been established that the increase in the efficiency of radar surveillance of airspace is currently being carried out due to the quantitative increase in radar stations. In this paper, to improve the quality of detection of subtle air objects, the use of multi-position location (based on MIMO systems) is proposed. The principle of operation of a spatially coherent MIMO system is considered. The set of spaced positions of a spatially coherent multi-position system is considered as a single sparse antenna array. The possibility of increasing the resolution in planar coordinates in a MIMO system compared to the resolution of a single autonomous radar station is shown. The calculated ratio for increasing the resolution of such a system is given. Detection curves are given for the case of a spatially coherent MIMO radar system for the case when the radar stations of the system operate simultaneously in the transceiver mode. It has been established that the transition from an autonomous radar station to a MIMO system leads to a significant shift in detection characteristics to the left. It has been established that an increase in the number of radar stations in the system leads to a less significant shift in the detection characteristics to the left, which indicates a decrease in the effect. The radar systems proposed by MIMO can be built on the basis of existing radar stations of a predominantly new fleet. It is especially advantageous to use radar facilities based on phased antenna arrays for these purposes. It is advisable to use such systems in particularly dangerous areas to cover important military, government and critical infrastructure facilities

Waveform design for target classification in MIMO radar systems

MIMO Radar sensors are relatively immune to environmental conditions and can work in complete darkness and also bright daylight. Also, because of ultra-high range resolution (ability to discriminate closely spaced objects) and sensitivity (ability to detect weak signals), these sensors are ideal for different applications, including healthcare and security, provided that appropriate transmit and receive strategies are employed in the sensor. Because of their high frequency, with appropriate signal processing, these sensors potentially can classify and sometimes identify different objects. In this paper, we propose a waveform design framework to enhance target classification performance for the MIMO radar systems. To this end, we enhance object discrimination by maximizing the differences between received signal from every target. We show that this difference depends to the radar transmit waveform. Thus, we design the probing signal, by solving a non-convex optimization problem while considering unimodular/discrete phase, and similarity constraints in the design stage. To solve the optimization problem, we propose Coordinate Descent (CD) framework, and iteratively design the code entries. The results show that the performance of the proposed framework enhances the discrimination performance for automotive MIMO radar systems.

Online Gain-Phase Self-Calibration Method of MIMO Array Based on Statistical Characteristics of Target Angle Distribution

As a hot research topic, the gain-phase error self-calibration in MIMO radar systems has been investigated for many years. In this paper, we proposed a novel array error self-calibration method, termed online errors self-calibration based on feature learning (OES-FL). This method regards the statistical characteristics of the detected targets’ DOA as a prior knowledge and does not require the calibrated antenna subarray or external reference source to correct the array disturbances in real time. First, we analyse the monostatic MIMO signal model suffering gain-phase error. Then, we exploit the statistical characteristics of DOA of many targets for correcting gain-phase error of antenna array. Next, the gain-phase error estimation scheme based on LMS and the DOA deviation estimation method based on LSTM are proposed, respectively. Using real-life radar data collected at the integrated transportation hubs to generate simulation data, the proposed approach is shown to be effective in correcting gain-phase errors and, therefore, provides a promising model for online error self-calibration in monostatic MIMO radars.

Pulsed-chaos MIMO radar based on a single flat-spectrum and Delta-like autocorrelation optical chaos source.

We propose and demonstrate a pulsed-chaos multiple-input-multiple-output (MIMO) radar system in this paper. In the proposed MIMO radar system, multi-channel pulsed chaotic signals are extracted from an optical seed chaos source with Delta-like autocorrelation and flat spectrum. The seed chaos source is generated by passing the chaotic output of an external-cavity semiconductor laser through a dispersive self-feedback phase-modulation loop and used for MIMO radar signal generation. The cross-correlation characteristics of MIMO radar signals, the maximum channel number of separable mixed echoes, as well as the performances of multi-target ranging and anti-interference in the proposed pulsed-chaos MIMO radar system are systematically investigated. The results indicate that multi-channel pulsed-chaos signals with Delta-like autocorrelation can be simultaneously generated from the seed chaos source, and excellent quasi-orthogonality of transmission radar signals can be guaranteed. Moreover, it is demonstrated that the proposed pulsed-chaos MIMO radar supports multi-target ranging with a centimeter-level resolution and can maintain satisfactory performance under low SNR scenarios with various interferences.

MIMO Radar Parallel Simulation System Based on CPU/GPU Architecture.

The data volume and computation task of MIMO radar is huge; a very high-speed computation is necessary for its real-time processing. In this paper, we mainly study the time division MIMO radar signal processing flow, propose an improved MIMO radar signal processing algorithm, raising the MIMO radar algorithm processing speed combined with the previous algorithms, and, on this basis, a parallel simulation system for the MIMO radar based on the CPU/GPU architecture is proposed. The outer layer of the framework is coarse-grained with OpenMP for acceleration on the CPU, and the inner layer of fine-grained data processing is accelerated on the GPU. Its performance is significantly faster than the serial computing equipment, and satisfactory acceleration effects have been achieved in the CPU/GPU architecture simulation. The experimental results show that the MIMO radar parallel simulation system with CPU/GPU architecture greatly improves the computing power of the CPU-based method. Compared with the serial sequential CPU method, GPU simulation achieves a speedup of 130 times. In addition, the MIMO radar signal processing parallel simulation system based on the CPU/GPU architecture has a performance improvement of 13%, compared to the GPU-only method.

Motion Compensation Method Based on MFDF of Moving Target for UWB MIMO Through-Wall Radar System

Ultrawideband (UWB) multiple-input-multiple-output (MIMO) radar is widely used for through-wall imaging (TWI) due to its excellent penetrability and large aperture. Multichannels in the MIMO radar system are usually time-division multiplexing based on microwave switches to reduce the complexity of the system in engineering. The switching process of the channel will bring time delay, which cannot be ignored in the TWI of the moving target. The switching time delay will cause the defocus and position shift of the TWI of the moving target. This letter proposes a motion compensation method based on multiframe data fusion (MFDF) used for correcting the echo of the through-wall moving target. A geometric model is established in the proposed method through the echo of the current frame and the next frame, and the compensated signal is obtained through the geometric solution. The proposed method is compared with before compensation and the traditional single-channel motion compensation algorithm (SCMCA) through simulation and experimental data verification. The visual images and quantitative results show that the proposed motion compensation method can obtain a good focus image of the through-wall moving target and reduce the positioning error.

A Two-Stage Support Vector Machine and SqueezeNet System for Range-Angle and Range-Speed Estimation in a Cluttered Environment of Automotive MIMO Radar Systems

This paper proposes a two-stage deep-learning approach for frequency modulated continuous waveform multiple‐input multiple‐output (FMCW MIMO) radar embedded in cluttered and jammed environments. The first stage uses the support vector machine (SVM) as a feature extractor that discriminates targets from clutters and jammers. In the second stage, the angle, range, and Doppler estimations of the extracted targets are treated by the SqueezeNet deep convolutional neural network (DCNN) as a multilabel classification problem. The performance of the proposed hybrid SVM-SqueezeNet method is very close to the one achieved by the SqueezeNet only but with the advantage of identifying the type of targets and reducing the training time required by the SqueezeNet.

Mutual Interference Mitigation for Multiple Connected Automotive Radar Systems

The number of commercial and civilian vehicles equipped with automotive radars is expected to rise rapidly in the forthcoming years and with that comes the problem of increased mutual interference between the radar sensors, which can result in severely reduced radar sensitivity and increased false alarm rates. The difficulty and complexity of the problem increase with MIMO radar systems and multiply even further with a growing number of vehicles present on the scene. A system of connected vehicles can efficiently address this problem by sharing information amongst themselves. In this paper, we propose an efficient waveform design algorithm that seeks to minimize a collective cross-ambiguity function. Vehicles that can <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">talk</i> to each other, can perform the design online in a collaborative manner, or offline, in which case the radar codes can be designed and stored in a codebook for future use. The proposed coding scheme is computationally efficient for practical use and the incorporation of such schemes requires only a slight modification of the existing systems. Our numerical examples indicate that the proposed scheme can significantly reduce the interference power level in a desired area of the radar cross-ambiguity functions.

Receive Space-Time Filter and Transmit Sequence Design in MIMO Radar Systems

Receive Space-Time Filter and Transmit Sequence Design in MIMO Radar Systems

In-Band Full-Duplex Dynamic Spectrum Sharing in Beyond 5G Networks

Dynamic spectrum sharing (DSS) has recently attracted keen interest from regulatory bodies around the world as a key strategy to overcome spectrum scarcity in beyond 5G networks. In the United States, the Federal Communications Commission has adopted a three-tier DSS model for the Citizens Broadband Radio Service (CBRS) band managed by a spectrum access system (SAS) to share incumbents' spectrum with commercial cellular broadband applications. In this article, we first present a survey of current DSS approaches and their limitations and then discuss the benefits of using in-band full-duplex (IBFD) techniques in DSS networks. To illustrate the improvement in the performance of priority access license (PAL) and general authorized access (GAA) users in an IBFD-assisted CBRS, we consider a CBRS mobile broadband network (MBN) architecture comprising an incumbent in the form of a MIMO radar system and an IBFD MIMO MBN, consisting of PAL and GAA users. In particular, we design joint beamformers at the MBN, with constraints on transmit power at the MBN and detection probability of the radar system, and beamformers at the radar system to mitigate the interference from the radar system toward the cellular system. It is shown that the IBFD CBRS network architecture not only leads to improved performance of PAL and GAA users, but does so while producing less interference toward the radar than state-of-the-art half-duplex solutions. Finally, we present some open research challenges to invigorate research on beyond 5G IBFD DSS networks.

Closed-form estimation algorithm for EMVS-MIMO radar with arbitrary sensor geometry

In this paper, we investigate into the problem of target localization in bistatic MIMO radar system with the prototype of arbitrary EMVS. A novel subspace algorithm is derived, which offers closed-form solution to 2D-DOD and 2D-DOA estimation. The core of the proposed algorithm is to construct the normalized Poynting vector via the vector cross-product of two special vectors, which consist of the rotation invariant factors of the array and can be estimated through the least squares technique. The proposed algorithm can provide automatically paired 2D-DOD, 2D-DOA, and polarization parameters estimation. Moreover, it is insensitive to the sensor position error. The proposed algorithm is analyzed in detail, and numerical simulations are provided to verify its effectiveness.

An Efficient Estimator for Source Localization Using TD and AOA Measurements in MIMO Radar Systems

3-D target localization based on multi-input and multioutput radar systems is studied in this letter by utilizing time delay and angle of arrival measurements, which can be obtained by some local sensor signal processing methods. For such a positioning problem, the typical closed-form solution first establishes pseudolinear equation and then, applies the weighted least squares (WLS) technique to determine the final position. However, the WLS solution often suffers from performance degradation when the noise is large. To alleviate this problem, the cost function of the WLS is represented, and a quadratic constraint is imposed such that the expectation of the cost function can attain the minimum value at the true position. Moreover, simulation experiments show that the proposed method performs better than the state-of-art algorithms.

Intelligent Reflecting Surface-Enhanced Target Detection in MIMO Radar

Motivated by the improved capacity and energy performance by intelligent reflecting surface (IRS) in multiinput-multioutput (MIMO) communication systems, we apply the IRS in the colocated MIMO radar systems to increase the received power and improve the target detection performance. To deal with this, we introduce an IRS-assisted target detection algorithm, in which the reflection optimization with practical IRS model is carried out, and then the optimal beamforming for target detection is proposed. Meanwhile, the Cramér-Rao bound (CRB) of the proposed method shows the effectiveness of utilizing the IRS in MIMO radar systems. Simulation results illustrate that the proposed method can achieve better target detection performance. With the increased number of elements in the IRS, the proposed method has better angle resolution and detection probability compared with a non-IRS-assisted MIMO radar system.