Multiple-input Multiple-output Radar Research Articles

Adaptive multiple targets detection for FDA-MIMO radar with Gaussian clutter

This paper presents an adaptive multiple targets detection framework for frequency diverse array multiple-input multiple-output radar embedded in Gaussian noise with an unknown covariance matrix. To this end, we define the one-range-cell multiple targets model as a summation expression and then design four detectors, i.e., the generalized likelihood ratio test, adaptive matched filter, Rao, and Wald tests for the above newly built problem. Closed-form expressions for the probability of false alarm and the probability of detection are provided to assess the aforementioned detectors. The numerical examples show that the proposed architectures can ensure better detection performance than the considered competitors. Finally, notice that each proposed detector has a specific behavior in terms of robustness to low volumes of data. Thus, the choice of a specific solution is dictated by the operating requirements of the radar system.

DOA Estimation via Tensor Truncated Convolution Nuclear Norm Minimization in MIMO Radar Under Array Sensor Failures

Exploring the inherent multidimensional structures has great potential to obtain more latent information for reconstructing the missing data resulting from array sensor failures in multiple-input–multiple-output (MIMO) radar. Most tensor completion (TC) methods based on the minimization of tensor nuclear norms are adopted to fill randomly distributed missing entries. However, the randomness of missing entries is violated in MIMO radar under sensor failures, where some slices are missing entirely. Thus, these existing methods become ineffective when faced with such type of structurally missing entries. To remedy this, we propose an improved TC method based on tensor truncated convolution nuclear norm minimization (CNNM) to reconstruct the failed sensor data in MIMO radar in order to handle sensor failure in direction-of-arrival (DOA) estimation. First, we formulate a new tensor by convoluting the third-order tensor of MIMO radar with a kernel tensor in a truncated manner and establish a low-rank TC model equipped with the truncated CNNM. Then, we introduce a tractable relaxation of our minimization function by deducing the equality relation between the truncated convolution nuclear norm of a tensor and the nuclear norm of the truncated convolution matrix of the same tensor. Finally, we derive an efficient algorithm implementation based on alternating direction method of multipliers (ADMM) to tackle our problem. Simulation results show that the proposed method is quite effective in completing tensors with structurally missing entries and facilitating more accurate data recovery by exploiting the inherent multidimensional nature. This provides superior DOA estimation performance in MIMO radar.

Contactless GSR Sensing Using mmWave Radar

The galvanic skin response (GSR), which is a manifestation of the activity in eccrine sweat glands, has always been an important psychological test to evaluate emotional arousal and mental stress. Although researches have focused on the radio reflections of skin and revealed that sweat gland activity can change the reflection signal of skin, the complicated experimental settings including vector network analyzer (VNA) and even optical path design limit further daily life application. In this article, we demonstrate the potential of using commodity millimeter-wave (mmWave) multiple-input multiple-output (MIMO) radar for contactless GSR sensing. To simulate the reflected signals of the frequency-modulated continuous-wave (FMCW) radar to the skin of different individuals, a four-layered skin model focused on the sweat duct is used and the change in conductivity in sweat ducts is regarded as mapping of sweat gland activity. Extensive experiments are conducted, consisting of three different physiological statuses: relaxing status, mental stress status, and physical stress status. We design a beamformer to transform the raw signal to the space domain so that the reflection signals from the palm are separated. The principal components of the processed signal are extracted to eliminate the interference caused by breathing and random noise. We evaluate the performance in terms of the correlation between the radar signal and the GSR before and after stress-induced events. The experimental results preliminarily verify the feasibility of contactless GSR sensing.

Beampattern synthesis with nulling for sub-arrayed MIMO radar under constant modulus constraints

Sub-arrayed multiple-input multiple-output (MIMO) radar holds the benefits of waveform diversity and coherent processing gain jointly. Based on prior knowledge, we exploit the sub-arrayed MIMO radar to form multiple beams in the desired directions and null the power toward the interference or clutter. Meanwhile, to maximize the radiation efficiency of nonlinear amplifiers in radar systems, the transmit weight vectors are required to have constant modulus properties, which leads to a complicated nonconvex problem. To that end, an iteration method with high calculation efficiency based on the alternating direction penalty method (ADPM) framework is presented to resolve this problem. The original problem is transformed into a general bi-convex problem with bi-affine constraints via introducing auxiliary variables. A high convergence rate can be achieved by selectively increasing the penalty parameters in each iteration. For any initialization, the ADPM-based method is proved to converge. And the obtained convergent solution fulfills the Karush-Kuhn-Tucker (KKT) conditions. Finally, the effectiveness of the proposed method is demonstrated by simulation results.

Subarrays of phased-array antennas for multiple-input multiple-output radar applications

The subarray MIMO radar (SMIMO) is a multiple-input multiple-output (MIMO) radar with elements in the form of a sub-array that acts as a phased array (PAR), so it combines at the same time the key advantage of the PAR radar, which is high directional gain to increase target range, and the key advantage of the MIMO radar, i.e., high diversity gains to increase the maximum number of detected targets. Different schemes for the number of antenna elements in the transceiver zones, such as uniform and/or variable, overlapped and non-overlapped, significantly determine the performance of radars as virtual arrays (VARs), maximum number of detected targets, accuracy of target angle, detection resolution, SNR detection, and detection probability. Performance is also compared with the PAR, the MIMO, and the phased MIMO radars (PMIMO). The SMIMO radar offers great versatility for radar applications, being able to adapt to different shapes of the multiple targets to be detected and their environment. For example, for a transmit-receive with an antenna element number, i.e., <em>M</em> = <em>N</em> = 8, the range of the number of detected targets for the SMIMO radar is flexible compared to the other radars. On the other hand, the proposed radar's signal-to-noise ratio (SNR) detection performance and detection probability (<em>K</em> = 5, <em>L</em> = 3) are both 1,999 and above 90%, which are better than other radars.

Integrated Sensor Fusion Based on 4D MIMO Radar and Camera: A Solution for Connected Vehicle Applications

This article presents an integrated sensor fusion (ISF) solution based on the multiple-input, multiple-output (MIMO) radar, camera, and on-device computing. The MIMO radar is capable of estimating an object’s attributes in four dimensions—range, velocity, azimuth angle, and elevation angle—which can be further used to estimate the length, width, and height of the object. The camera is responsible for object classification based on deep learning. The respective signal processing pipelines and the fusion of results are carried by the on-device computing platform. These two sensors complement each other very well in detecting and classifying traffic objects. Compared with existing sensor fusion solutions based on multiple distributed devices, ISF exhibits superior performance in terms of latency and the total cost of ownership (TCO). It also simplifies time synchronization among different sensors and facilitates the deeper fusion of the signal processing algorithms of different sensors. The comprehensive roadside sensing capabilities provided by the ISF solution can enhance the safety and efficiency of both the automated driving and human driving of connected vehicles.

A Single-Snapshot Localization for Monostatic FDA-MIMO Radar

We propose a low-complexity method which estimates the angles and the ranges of targets, using single-snapshot of frequency diverse array-multiple input multiple output (FDA-MIMO) radar. Existing single-snapshot-based methods apply one-dimensional discrete Fourier transform (DFT) to a received signal which is made of superposition of Vandermonde structured steering vectors. However, such method is not directly applicable to FDA-MIMO, as joint steering vector of the FDA-MIMO does not obey Vandermonde structure. To tackle this problem, the proposed method alters the signal vector to the form to which two-dimensional DFT (2D-DFT) is applicable for the coarse estimates. Afterwards, precise version is extracted by searching small region around the initial estimates. Numerical results demonstrate superiority of the proposed method over the phased-array-based method in separating targets with close angles. Furthermore, it is proved that the proposed method is more suitable to single-snapshot-based localization than the conventional methods, maintaining low complexity.

Domain Knowledge-Assisted Deep Reinforcement Learning Power Allocation for MIMO Radar Detection

The power allocation of multiple-input multiple-output (MIMO) radars is a key point in target tracking and detection. The optimality of a multitarget and multiconstraint optimization problem strictly depends on a priori model, which is difficult to obtain in time-varying, complex, and noncooperative environments. Recently, deep reinforcement learning (DRL) has been applied for target tracking tasks, which provides a trial-and-error interactive learning mechanism to improve the policy. Unlike tracking tasks with complete target state transition models, it remains an open issue for DRL-based MIMO radar detection that requires efficiently adapting the control policy to the environment of randomly appearing targets and extensive power transmission actions, which leads to sparse final task rewards and hence slow policy learning for the agent. Through introducing both the analytic model (radar equation) and empirical rules (expert preferences) for domain knowledge, this article proposes a domain-knowledge-assisted DRL (DKADRL) framework in which a domain-knowledge-based timely reward generator is utilized to generate timely rewards that assist the agent’s policy learning. To adjust the role of the timely rewards and the final task rewards, a reward fusion module is designed, which gradually increases the role of the final task rewards as the training process progresses, thus allows agent’s policy to converge to the final optimization goal. The algorithm is validated under two target motion scenarios, showing the higher target detection probability and the faster training speed, compared to equal power allocation and proximal policy optimization (PPO)-based power allocation.

Enhancement of a State-of-the-Art RL-Based Detection Algorithm for Massive MIMO Radars

In the present article, a reinforcement learning (RL)-based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">adaptive</i> algorithm to optimize the transmit beampattern for a colocated massive multiple-input multiple-output (MIMO) radar is presented. Under the massive MIMO regime, a robust Wald-type detector, able to guarantee certain detection performances under a wide range of practical disturbance models, has been recently proposed. Furthermore, an RL/cognitive methodology has been exploited to improve the detection performance by learning and interacting with the surrounding unknown environment. Building upon previous findings, we develop here a fully adaptive and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data-driven</i> scheme for the selection of the hyperparameters involved in the RL algorithm. Such an adaptive selection makes the Wald-RL-based detector independent of any <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ad hoc</i> , and potentially suboptimal, manual tuning of the hyperparameters. Simulation results show the effectiveness of the proposed scheme in harsh scenarios with strong clutter and low SNR values.

Angle Estimation for Bistatic MIMO Radar Using One-Bit Sampling Via Atomic Norm Minimization

In this article, we focus on the bistatic multiple-input multiple-output (MIMO) radar that performs one-bit quantization of received echo data. Based on the one-bit bistatic MIMO radar, we devise an effective algorithm for joint direction-of-departure (DOD) and direction-of-arrival (DOA) estimation. By exploiting the signal sparsity, we first formulate a decoupled atomic norm minimization problem to reconstruct two noise-free covariance matrices from one-bit quantized data. Subsequently, we apply the MUSIC algorithm on two reconstructed matrices to estimate the DODs and DOAs of targets, respectively. Furthermore, an additional procedure is proposed to achieve pairing of the estimated DODs and DOAs. The proposed algorithm can achieve satisfactory estimation performance only using single-pulse data. Numerical results demonstrate the effectiveness of the proposed algorithm.

A Novel Approach to Unambiguous Doppler Beam Sharpening for Forward-Looking MIMO Radar

The ambiguity problem of targets in Doppler beam sharpening (DBS) with forward-looking radar is considered. While DBS is proposed earlier to improve the angular resolution of the radar while keeping the antenna aperture size limited, such a solution suffers from ambiguities in the case of targets positioned symmetrically with respect to the platform movement. To address this problem, an approach named unambiguous Doppler-based forward-looking multiple-input multiple-output (MIMO) radar beam sharpening scan (UDFMBSC) is proposed, based on the combination of MIMO processing and DBS. The performance of the proposed method is compared to existing approaches using simulated data with point-like and extended targets. The method is successfully verified using experimental data.

A multi-frame space-time coding method for the phase-coded signal in MIMO radars

Code-division multiplexing (CDM) has become the focus of designing orthogonal waveforms of multiple-input multiple-output (MIMO) radars as it occupies fewer resources than time-division multiplexing (TDM) and frequency-division multiplexing (FDM). As one of the standard CDM waveforms, conventional inter-pulse phase-coded waveform possesses non-ideal diversity performance. That is, the orthogonality of the signals will reach the threshold after the codes extend to a certain length. To circumvent the coding restrictions, space-time coding (STC) makes use of spatial resources. This paper proposes a multi-frame STC method to improve the orthogonality among waveforms. Specifically, we first design the orthogonal transmitted signals and corresponding matched filters for MIMO radars. Then the signal expressions and the radar ambiguity function (AF) are deduced to verify the proposed method theoretically. Compared with conventional STC, such a method can save half of the time domain resources and make the most of coding resources. Furthermore, the output signal-to-noise ratio (SNR) is well improved as the proposed method effectively suppresses the cross-correlation energy of the waveform. The proposed multi-frame STC waveform can be easily implemented in practical radar applications because of its constant waveform amplitude. The theoretical analysis and simulation results demonstrate the effectiveness of the method.

Multiple-input multiple-output radar, ground-based MIMO SAR for ground deformation monitoring

ABSTRACT This study focuses on investigating the capabilities of a Multiple-input multiple-output RADAR. A Radar interferometer, based on an electronically scanned array in MIMO configuration (MIMO‐SAR), has been assessed for operational use in monitoring phenomena of geological interest, such as landslides unstable slopes. The system applies the very well‐known and proven Ground-Based Interferometric technique. It guarantees a very short refreshing time compared to traditional systems based on the mechanical movement of the radar transceiver on a rail or the mechanical steering of a real antenna. The system can monitor several phenomena having deformation rates too high to be correctly retrieved by traditional systems currently in use. Implementing a prototype termed MELISSA allowed the testing technique’s performances in two real case studies: a landslide and an unstable volcanic flank. The experimental results were compared with LISA, a well-known Ground-Based Interferometric Synthetic Aperture Radar (GBInSAR) interferometer. MELISSA allows for obtaining an excellent accuracy, better than 0.01 mm. The range and angular resolution are on the same order of magnitude as those obtained through LISA. However, the refreshing rate obtained from MELISSA, 0.01 s, guarantees a strong coherence even in challenging environmental scenarios as a flank of an active volcano.

DCFDA-MIMO: A dual coprime FDA-MIMO radar framework for target localization

Frequency diverse array multiple-input multiple-output (FDA-MIMO) radar has a range-angle-dependent beampattern by adding the frequency offset across array elements, which can simultaneously locate the target angle and range. In this paper, a novel dual coprime FDA-MIMO (DCFDA-MIMO) radar framework is proposed. It employs a set of coprime frequency offset at the transmitter and receiver to increase the frequency degrees of freedom (DOFs) and improve range estimation performance. Meanwhile, it overcomes the single snapshot problem of conventional coprime arrays in target parameters estimation, and further improves the selectivity of the algorithm. Furthermore, the Cramér-Rao bound and the analytical solution of maximum unambiguity range under this framework are given. In order to solve the range ambiguity problem, the subarrays DCFDA-MIMO structure is designed. The applicability of the proposed framework is proved by numerical simulations using the 2D-MUSIC method, which further demonstrates the superiority of the angle-range resolution and the distinguishable number of the targets.

Optimised radar waveform design based on the relative entropy constraint

A new strategy to optimise the waveform of multiple-input multiple-output (MIMO) radar under the relative entropy constraint and signal-to-noise ratio (SNR) constraint is presented. The Kullback–Leibler divergence (KLD) is exploited between the two probability density functions of binary hypothesis testing as the design metric. A particular analysis of detection performance is made here to illuminate the balance between the false alarm and missing alarm probabilities. The results of the simulation demonstrate that the waveform design process constrained by relative entropy outperforms the process employing an explicit SNR constraint when the SNR is relatively low (i.e., within the interval [−2 dB, 0.5 dB]). The enhanced performance is attributable to the capability of the proposed optimisation process to focus the energy of the transmission waveform onto components where the eigenvalues of the target signal are large and the eigenvalues of the noise are small.

Research on Multi-Domain Dimensionality Reduction Joint Adaptive Processing Method for Range Ambiguous Clutter of FDA-Phase-MIMO Space-Based Early Warning Radar

The ground and sea clutter received by space-based early warning radar (SBEWR) has severely range ambiguous characteristics due to its platform location, and the non-stationary factor caused by Earth’s rotation makes the received clutter at different range ambiguous positions seriously broaden in the Doppler dimension. The complex clutter suppression performance of SBEWR obtained by traditional method is degraded significantly. To solve this problem and achieve better clutter suppression performance, a novel multi-domain adaptive processing method for clutter suppression is proposed in this paper. The proposed method introduced a range related signal processing domain based on conventional space–time domain by using frequency diverse array phase multiple-input multiple-output (FDA-Phase-MIMO) radar. In addition, a novel multi-domain joint dimensionality reduction structure was designed. The novel multi-domain joint adaptive processing using the proposed dimensionality reduction structure could not only obtain great clutter suppression performance of SBEWR, but also minimize the requirement of the number of selected auxiliary channels. Simulation examples show the effectiveness of the proposed method.

Spectral-Compatible Transmit Beampattern Design With Minimum Peak Sidelobe for Narrowband MIMO Radar

Spectral-compatible radar emission has a paramount importance in realizing the coexistence of radar with wireless communications. In this paper, we deal with the design of spectral-compatible transmit beampattern with minimum peak sidelobe level for a narrowband multiple-input multiple-output(MIMO) radar. We also consider constraints that are imposed due to the practical high power amplifier(HPA) used in modern radars. The constraints are used to control the dynamic range on the magnitude of each signal, to control variations in the transmit power from the individual antennas and the total energy of the signals. The considered problem is challenging as it is non-convex. To this end, we propose an iterative solution that incorporates the majoriztion-minimization(MM) and alternating direction method of multipliers(ADMM) frameworks. Our solution avoids the computationally expensive operations such as matrix inverse and eigenvalue decomposition. We provide numerical results to validate the proposed solution. The results show that the method provides the intended spectral-compatible beampattern while maintaining a beampattern synthesize performance that is comparable to the state-of-the-art work.

MIMO Radar testbed based on USRP N320/321 software-defined radios

In a conventional phased array radar system (PAR), a transmit beam is slowly steered through a sector to illuminate targets sequentially, leaving the radar blind to threats when the transmit beam is pointed in other directions. On the other, multiple input, multiple output (MIMO) radar systems transmit orthogonal waveforms from each antenna element to illuminate the entire field of view (FOV) of the radar simultaneously, providing uninterrupted situational awareness. However, MIMO radar systems are more challenging to implement because of the added orthogonality requirements between its channels and the drastic increase in the required digital signal processing. This paper uses commercially-available cutting-edge software-defined radios (SDRs) to build a reconfigurable 8 × 8 MIMO radar with a 64-element virtual array operating in C-band (NATO G-band). Measurements indicate that the radar can detect and localize multiple targets in all 3-dimensions, including bearing, range, and Doppler, with an angular resolution of 5.5 and a range resolution of 0.9 m. The MIMO radar operates in real-time with a refresh rate of only 3 s and can switch between TDM and CDM multiplexing modes.

Cognitive Sparse Imaging Method for MIMO Radar under Wideband Interference

Target three-dimensional (3D) high-resolution imaging via multiple-input multiple-output (MIMO) radar may suffer from a heavy sampling burden and complicated radio frequency interferences. Considering a collocated two-dimensional wideband MIMO radar under dynamic wideband interference (WBI), this paper proposes a cognitive method to achieve a 3D high-resolution target image with a reduced sampling cost. Firstly, based on the known knowledge of the target and WBI, provided by previous measurements, optimal sparse sampling in the 3D signal domain is conducted to reduce the number of sub-pulses and transceiving antennas by solving an optimization problem. Then, the detection and removal of the interfered signal components are conducted to provide the WBI information for following measurements and the interference-free signal cube for the target imaging process. Finally, by using the tensor-based smoothed L0 algorithm, the 3D high-resolution image of the target is obtained, providing the target information for the next measurement. Based on these three steps, a cognitive sparse imaging loop is formed for MIMO radar under WBI situations. The simulation and experiment results demonstrate the effectiveness and advantage of the proposed methods.

Field Trial of a Coherent, Widely Distributed, Dual-Band Photonics-Based MIMO Radar With ISAR Imaging Capabilities

Soon after its introduction in the communications domain, the novel concept of multiple input–multiple output (MIMO) has been making its way also into the radar world. A new generation of multistatic netted systems with unprecedented capabilities has been fully theorised, unveiling its potential advantages over its classical stand-alone, monostatic counterparts. However, MIMO radars mainly remained the object of abstract modeling, since electronic technology could hardly support the practical implementation of this new class of systems. With the development of microwave photonics, the realization of MIMO radar networks at the best of their capabilities has become a reality, thanks to the inherent coherence of photonics systems, and to the broad-band, low-distortion, and interference-immune optical signal distribution. This paper presents the results of a microwave photonics widely-distributed, dual-band MIMO radar network, deployed in a real freight port for maritime traffic monitoring, with inverse synthetic aperture radar imaging capabilities. It employs a central unit connected to multiple widely distributed remote radar peripherals thanks to optical fiber. The possibility to operate in dual-band mode, and the coherent management of the transmitted and received signals are demonstrated, exploiting geometric and frequency diversity in target detection. The system exhibits a spurious-free dynamic range of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{\sim 82}\,{\mathbf{d}\mathbf{B}\cdot \mathbf{\mathbf{Hz}}^{\mathbf{2/3}}}$</tex-math></inline-formula> and a sensitivity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf{-110}\,\mathbf{dBm}$</tex-math></inline-formula> . Under these premises, a small boat of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf \sim\! 1\,{\mathrm{\mathrm{m}}^{\mathrm{2}}}$</tex-math></inline-formula> radar cross section has been detected at more than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf {800}\,{\mathbf{m}}$</tex-math></inline-formula> , achieving a probability of detection of about <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf {0.85}$</tex-math></inline-formula> , for a false alarm rate of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf {7.7\times 10^{-4}}$</tex-math></inline-formula> .