Multiple-input Multiple-output Radar Research Articles

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.

Multitarget Enumeration and Localization in Distributed MIMO Radar Based on Energy Modeling and Compressive Sensing

Target localization is one of the most important research topics in the field of radar signal processing. In this paper, the problem of multitarget enumeration and localization in the distributed multiple-input multiple-output (MIMO) radar with noncoherent processing mode is investigated. We first analyze the theoretical bound of the multitarget localization accuracy under the discrete time signal model and the Swerling 1 target model. It is determined by the Cram'r-Rao lower bound (CRLB) at low signal-to-noise ratio (SNR) and the sampling lower bound (SLB) when the SNR is high. Furthermore, an innovative multitarget enumeration and localization scheme is developed, which is based on the energy modeling of the multiple transmitter-receiver paths and the compressive sensing theory. To solve the sparse vector recovery issue, we design a lightweight iterative greedy pursuit algorithm including the similarity evaluation strategy. In addition, an iterative-based target position refinement process is designed to alleviate the off-grid problem caused by the spatial discretization. The proposal utilizes the samples of the raw signals and belongs to the category of the direct localization. Nevertheless, it has significantly higher computational efficiency and lower data communication burden than the conventional direct localization methods, while avoiding the complex data association encountered by the indirect localization methods. Finally, the simulation results validate the effectiveness and robustness of the proposed method.

Distributed MIMO Radar Adaptive Detection in the Presence of Spectral Symmetry

We derive several parametric detectors for the case of a ground-based distributed multiple-input multiple-output (MIMO) radar system with co-located transmitters and receivers. The proposed detectors are based on exact and approximate generalized likelihood ratio tests. For the transmit-receive paths which start and end at the same node, we incorporate the constraint of spectral symmetry, meaning that the clutter returns are assumed to have a power spectral density that is symmetric around the zero Doppler, which is motivated by statistical analyses performed on measured clutter radar data. Our numerical simulations suggest that the proposed parametric detectors, which incorporate the physics-based spectral symmetry constraint on clutter returns, achieve better performance than previously investigated parametric detection approaches which do not incorporate this constraint.

Polynomial rooting-based parameter estimation for polarimetric monostatic MIMO radar

Compared with conventional multiple-input multiple-output (MIMO) radar, polarimetric MIMO radar has been proven to hold the additional capability to operate data in the joint spatial and polarization domains. However, practical transceivers may involve antenna arrays with classical non-ideal factors (e.g., gain/phase/position errors) and polarimetric non-ideal factors (e.g., polarimetric antenna misorientation), and many current approaches cannot handle such non-ideal factors in real applications. In this study, polynomial rooting-based direction-of-arrival (DOA) and polarization parameter estimation is investigated for polarimetric monostatic MIMO radar with non-ideal transceivers. By incorporating the polarimetric manifold separation (PMS) technique, a joint DOA and polarization parameter estimation method, termed PMS2, is proposed with a closed-form solution. The Cramér-Rao bound for the multi-dimensional parameters involved in the estimation problem is also given. Simulation results demonstrate the performance advantages of the proposed PMS2 approach, which is attributed to the compact structure of polarimetric transceivers and the ability of the PMS2 approach to handle the polarimetric properties of signals.

Suppression of Mainlobe Jammers with Quadratic Element Pulse Coding in MIMO Radar

The problem of suppressing mainlobe deceptive jammers, which spoof radar systems by generating multiple false targets, has attracted widespread attention. To tackle this problem, in this paper, the multiple-input multiple-output (MIMO) radar system was utilized by applying a quadratic element phase code (QEPC) to the transmitted pulses of different elements. In the receiver, by utilizing the spatial frequency and Doppler frequency offset generated after decoding, the jammers were equivalently distributed in the sidelobes of the joint Doppler-transmit-receive domain and were distinguishable from the true target. Then, further spatial frequency compensation and Doppler compensation were performed to align the true target to the zero point in the transmit spatial and Doppler domains. Moreover, by designing appropriate coding coefficients, the jammers were suppressed by data-independent Doppler-transmit-receive three-dimensional beamforming. However, the beamforming performance was sensitive to angular estimation mismatches, resulting in performance degradation of jammer suppression. To this end, a center-boundary null-broadening control (CBNBC) approach was used to broaden the nulls in the equivalent beampattern by generating multiple artificial jammers with preset powers around the nulls. Thus, the false targets (FTs) with deviations were sufficiently suppressed in the broadened notches. Numerical simulations and theoretical analysis demonstrated the performance of the developed jammer suppression method.

SMSP Mainlobe Jamming Suppression with FDA-MIMO Radar Based on FastICA Algorithm

In the electronic warfare environment, the performance of ground-based radar target search is seriously degraded due to the existence of smeared spectrum (SMSP) jamming. SMSP jamming is generated by the self-defense jammer on the platform, playing an important role in electronic warfare, making traditional radars based on linear frequency modulation (LFM) waveforms face great challenges in searching for targets. To solve this problem, an SMSP mainlobe jamming suppression method based on a frequency diverse array (FDA) multiple-input multiple-output (MIMO) radar is proposed. The proposed method first uses the maximum entropy algorithm to estimate the target angle and eliminate the interference signals from the sidelobe. Then, the range-angle dependence of the FDA-MIMO radar signal is utilized, and the blind source separation (BSS) algorithm is used to separate the mainlobe interference signal and the target signal, avoiding the impact of mainlobe interference on target search. The simulation verifies that the target echo signal can be effectively separated, the similarity coefficient can reach more than 90% and the detection probability of the radar is significantly enhanced at a low signal-to-noise ratio.

PSWF-based decoupled atomic norm minimization for DOD and DOA estimation in MIMO radar with arbitrary linear arrays

Decoupled atomic norm minimization (D-ANM) is a computationally-efficient gridless two-dimensional parameter estimation method via dividing the two-level Toeplitz matrix into two one-level matrices, and can be applied to the one-snapshot direction-of-departure (DOD) and direction-of-arrival (DOA) estimation in bistatic multiple-input multiple-output (MIMO) radar with uniform linear arrays (ULAs). Nevertheless, in actual applications, the transmitting and receiving arrays are not always ULAs, which leads to the Toeplitz structure not satisfied and the regular D-ANM algorithm not available. In this work, the one-snapshot DOD and DOA estimation in bistatic MIMO radar with arbitrary linear arrays (ALAs) is investigated. The Prolate spheroidal wave functions (PSWFs) are utilized to express such a D-ANM problem followed by a semi-definite programming (SDP) solution. The PSWF-based method can extend the application of D-ANM from ULAs to ALAs, and avoid the grid partition of the conventional sparse representation methods. The numerical experiments are conducted to verify the proposed method.

Distributed Localization of Target for MIMO Radar With Widely Separated Directional Transmitters and Omnidirectional Receivers

Conventional target localization algorithms for the multiple-input multiple-output (MIMO) radar with widely separated antennas are mainly based on the centralized localization framework, which suffer from huge burdens of computation and communication as well as robustness defect in complex environments. To overcome these issues, this paper first considers the MIMO radar with widely separated directional transmitters and omnidirectional receivers generating the bistatic range (BR) and angle of incidence (AOI) measurements for target localization, which is proven that the position of the target can be uniquely determined with one transmitter and one receiver. Then, the distributed localization framework using the hybrid BR and AOI measurements is developed, where each receiver acts as a fusion center extracting the BR and AOI measurements to perform the localization process, and the localization problem is formulated as solving a linear matrix equation. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">distributed constrained total least squares</i> (DCTLS) algorithm considering errors in both data matrix and observation vector is proposed for each receiver for target localization. The localization performance regarding the Cramér-Rao lower bound (CRLB) is derived for theoretical analysis. Numerical simulations are performed to validate the efficacy and superiority of the proposed DCTLS algorithm over other typical localization algorithms.

Phase-Coded FMCW for Coherent MIMO Radar

The phase-coded linear-frequency-modulated continuous-wave (PC-FMCW) waveform with a low sampling processing strategy is studied for coherent multiple-input multiple-output (MIMO) radar. The PC-FMCW MIMO structure, which jointly uses both fast-time and slow-time coding, is proposed to reduce sidelobe levels while preserving high range resolution, unambiguous velocity, good Doppler tolerance, and low sampling needs. The sensing performance and practical aspects of the introduced PC-FMCW MIMO structure are evaluated theoretically and verified experimentally. The numerical simulations and experiments demonstrate that the proposed MIMO keeps the advantages of the linear-frequency-modulated continuous-wave (LFMCW) waveform, including computational efficiency and low sampling demands, while having the ability to provide low sidelobe levels with simultaneous transmission.

Optimization of LPI-FDA-MIMO Radar and MIMO Communication for Spectrum Coexistence

This paper studies the spectral efficiency of a multiple-input multiple-output (MIMO) radar with frequency diverse array (FDA) and a MIMO communication system in the same frequency band. To suppress mutual interference for both systems, and enhance low probability of intercept (LPI) capability of radar system, we propose a joint design of the transmit waveform and receive filter of radar, and the code-book employed by the communication system to form its space-time code matrix. Our problem formulationis to maximize the signal-to-interference-plus-noise ratio (SINR) and simultaneously minimize the probability of target detection of the radar signal under the peak-to-average ratio and communication rate constraints. Since the design problem is non-convex, we first transform it into three sub-optimization problems. An iterative algorithm is then developed to solve these subproblems to obtain the solution of the original problem. Simulation results show that the devised approach can effectively reduce the mutual interference as well as increase the SINR and LPI for radar system.

Adaptive Target Detection With Polarimetric FDA-MIMO Radar

The problem of adaptive radar detection with a polarimetric Frequency Diverse Array Multiple-Input Multiple-Output (FDA-MIMO) radar is addressed in this paper. At the design stage, the target detection problem is formulated as a composite hypothesis test, with the unknowns given by the target angle, incremental range (target displacement with respect to the center of the occupied range cell), and scattering matrix, as well as the interference covariance matrix. The formulated detection problem is handled by resorting to sub-optimal design strategies based on the Generalized Likelihood Ratio (GLR) criterion. The resulting detectors demand, under the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_\mathrm{{1}}$</tex-math></inline-formula> hypothesis, the solution of a box-constrained optimization problem for which several iterative techniques, i.e., the Linearized Array Manifold (LAM), the Gradient Projection Method (GPM), and the Coordinate Descent (CD) algorithms, are exploited. At the analysis stage, the performance of the proposed architectures, which ensure the bounded CFAR property, is evaluated via Monte Carlo simulations and compared with the benchmarks in both white and colored disturbance.

Deep Learning-Based In-Cabin Monitoring and Vehicle Safety System Using a 4-D Imaging Radar Sensor

In this paper, we use a multiple-input multiple-output (MIMO) radar for in-vehicle passenger detection. We propose a 2D CNN-LSTM (Convolutional Neural Network- Long Short-Term Memory) to accurately detect, count and classify passengers inside five-seater vehicles. Our deep learning model first extracts feature using a CNN model, and then a series of frames will be delivered to a time series model (LSTM) to predict new scenarios. Additionally, we provide the outcomes of various deep learning models and show that temporal deep learning models perform better in our radar datasets. Furthermore, we provide reliable session-dependent datasets collected from different car models with various passengers (including infants/ children and adults). The results show that our proposed 2D CNN-LSTM model can detect unattended infants/ children in vehicles with more than 95% accuracy, count passengers and identify their occupied seats with an accuracy of 89% and classify passengers with more than 74% accuracy. Since our model is evaluated in a new car with new passengers, it ensures the generality of our proposed method to be deployed in any five-seater vehicle.

Explicit bounds for joint range and direction-of-arrival estimation in MIMO radar

Bayesian bounds, such as the Ziv–Zakai bound (ZZB), can provide a tight baseline for the performance of estimators or an accurate prediction for the performance limit of radar systems under all signal-to-noise ratio (SNR) conditions. However, there exists very limited literature that deals with the explicit bound for joint estimation of multi-parameters in radar systems. In this paper, we employ the thought of Shannon’s information theory to propose a tight bound with closed-form expression for joint range and direction-of-arrival (DOA) estimation in multiple-input multiple-output radar. Based on the joint a posteriori probability density function, we define range-DOA entropy error (RDEE) as the entropy power of range and DOA. With normalized a posteriori entropies in low and high SNRs, the closed-form approximation for RDEE is derived. The explicit RDEE is comprehensive and captures the effect of the SNR, the number of array elements, and the ratio of the signal bandwidth to the carrier frequency. RDEE is compared with the ZZB by a numerical simulation and the result shows RDEE and ZZB are almost identical in low and high SNRs.

Space time adaptive processing for airborne MIMO radar based on space time sampling matrix

Different from the traditional airborne single-input multi-output (SIMO) radar, each array element of airborne multiple-input multiple-output (MIMO) radar transmits mutually orthogonal waveforms, and multiple array elements receive at the same time. A set of matched filters are used to separate the transmitted waveforms in the echo signal. When the elements of the transmitting antenna are close to each other, the airborne MIMO radar can gain the benefit of waveform diversity and produce a large spatial virtual receiving array. It has certain advantages in signal-to-noise ratio, spatial degree of freedom (DoF) and spatial resolution. In this paper, the clutter DoF estimation formula of airborne MIMO radar is given and can accurately estimate the clutter DoF values under different parameters. On this basis, this paper proposes a space-time adaptive processing (STAP) method based on space-time sampling matrix. This method utilizes the low rank characteristic of clutter covariance matrix, constructs the clutter covariance matrix based on space-time sampling matrix, and estimates the clutter power through subaperture smoothing. In the non-homogeneous clutter background, the method only needs a single sample to effectively suppress clutter. In the homogeneous clutter background, as the space-time equivalent array is a dense non-uniform array, the robustness of the method is further improved by adding a small amount of training sample information.