Multiple-output Radar Research Articles

Joint Design Method of Transmit-Receive for Airborne MIMO Radar Based on Feasible Point Pursuit

Consider the moving target detection performance degradation of airborne multiple-input multiple-output (MIMO) radar in the presence of inaccurate target prior information. This paper proposes a joint design method of transmit waveform and receive filter bank of airborne MIMO radar based on feasible point pursuit successive convex approximation (FPP-SCA). Firstly, a set of receive filter banks is designed in the region where the target may appear on the angle-Doppler plane, and the worst-case output signal-to-clutter-plus-noise ratio (SCNR) is maximized as the optimization criterion. Secondly, considering the energy constraint and similarity on the transmit waveform, the maximin joint design problem is formulated to improve the robustness of the MIMO space-time adaptive processing (STAP) radar against the uncertainty of target parameters. Finally, an FPP-SCA algorithm is employed to solve the maximin nonconvex joint design problem. Simulation results demonstrate the effectiveness of the proposed method in terms of better output SCNR, lower computational load, and more robustness against the errors of target parameters.

On Parameter Identifiability of Diversity-Smoothing-Based MIMO Radar

Diversity smoothing has been widely used for angle estimation with multiple input multiple output (MIMO) radar in the presence of coherent or correlated targets, and the parameter identifiability is an interesting issue. Previous works have shown sufficient conditions for some special cases, such as the coherent targets with conventional MIMO radar, and the array size are derived as a function of the target number and target structure. In this article, we further introduce the interelement spacings to build a complete parameter identifiability scheme. For monostatic MIMO radars, the antenna numbers and interelement spacings of transmit and receive arrays are derived as functions of the target number and the target structure. The optimization models are built to calculate the maximum number of detectable targets for a given target structure. Additionally, the conditions for the bistatic MIMO radar are derived from two-dimension viewpoint. It is shown that the new results improve upon previous spatial smoothing or diversity smoothing methods and recover them in special cases. Simulation results are presented that corroborate our theoretical findings.

A Comparison between Multiple-Input Multiple-Output and Multiple-Input Single-Output Radar Configurations for Through-the-Wall Imaging Applications

The performances of a multiple-input multiple-output (MIMO) radar, employing 16 equivalent antennas, and multiple-input single-output (MISO) radar, employing 10 antennas, for through-the-wall imaging applications are analyzed. In particular, imaging algorithms based on the Fourier transform (FT) and the multiple signal classification (MUSIC) available in the literature are compared with the FT-MUSIC hybrid algorithm recently developed by the authors. Three different investigations have been performed. The first, performed analytically, refers to a scenario in which a point scatterer is placed in free space, and the second, addressed numerically using the CST full-wave software, refers to a scenario in which two targets are present, while the last was executed in a real scenario where a metal panel is placed behind a tuff wall. All the algorithms and radar configurations were found to be suitable for accurately reconstructing the position of the investigated target. In particular, applying the FT technique, the MISO configuration has a lower cross-range half-power beamwidths (HPBW) than the MIMO one, while the range HPBW is the same for the two radar configurations. Despite the different number of elements present in the two radar configurations, similar range and cross-range HPBW are obtained for both configurations when MUSIC and FT-MUSIC techniques are employed. The field of view for FT and FT-MUSIC is about 45°, while it is less than 15° for the MUSIC algorithm. The HPBWs obtained with the experimental setup are very close to those obtained in the analytical study. Finally, the proposed experimental MISO radar acquires the data in half the time required by the MIMO one. The numerical results, confirmed by the experimental measurements, seem to indicate in the FT-MUSIC technique the one that provides the best performance for the considered radar configurations.

Joint DOD and DOA Estimation of Bistatic MIMO Radar for Coprime Array Based on Array Elements Interpolation

This paper proposes a method to address the problem of the joint direction of departure (DOD) and direction of arrival (DOA) estimation with augmented coprime array (CPA) bistatic multiple input multiple output (MIMO) radar using interpolating sensors. At first, we deduce the regular pattern of hole positions in the virtual array and interpolate a small number of sensors to augmented CPA to form a partially contiguous virtual array. Then, we use the diversity smoothing algorithm to reconstruct the Toeplitz matrix to obtain a spatial smoothing matrix. Finally, we combine the RD-MUSIC algorithm with the spatial smoothing matrix to estimate the spatial spectrum and achieve automatic matching of DODs and DOAs for the targets. Simulation results clearly illustrate the superiority of the method.

Height measurement method of monostatic metre wave multiple input multiple output radar based on coprime array

The height measurement algorithm of metre wave multiple input multiple output (MIMO) radar based on uniform linear array (ULA) has some problems, such as a sharp decline of angle measurement accuracy of ultra-low altitude targets and large fluctuation of height measurement with the change of elevation. In order to solve the above difficult problems, starting from the perspective of reducing the influence of multipath effect by array structure, coprime array (CPA) is applied to the height measurement of monostatic metre wave MIMO radar. In this paper, the signal model of monostatic CPA metre wave MIMO radar is established under the condition of flat ground, a height measurement method using physical array is proposed based on the derivation that it is infeasible to use virtual array method for height measurement, it fills the gap of low elevation estimation method based on sparse array metre wave MIMO radar. The proposed method calculates the echo data according to the characteristics of the steering vector of CPA, then obtains the accurate low elevation by using the generalised multiple signal classification algorithm, and finally the target height is obtained by using the geometric relationship. Simulation experiments compare three typical CPAs with ULA using equal array elements, and verify the superiority of height measurement performance of monostatic metre wave MIMO radar based on CPA and the effectiveness of the proposed method.

Deep Unfolding of Iteratively Reweighted ADMM for Wireless RF Sensing.

We address the detection of material defects, which are inside a layered material structure using compressive sensing-based multiple-input and multiple-output (MIMO) wireless radar. Here, strong clutter due to the reflection of the layered structure’s surface often makes the detection of the defects challenging. Thus, sophisticated signal separation methods are required for improved defect detection. In many scenarios, the number of defects that we are interested in is limited, and the signaling response of the layered structure can be modeled as a low-rank structure. Therefore, we propose joint rank and sparsity minimization for defect detection. In particular, we propose a non-convex approach based on the iteratively reweighted nuclear and -norm (a double-reweighted approach) to obtain a higher accuracy compared to the conventional nuclear norm and -norm minimization. To this end, an iterative algorithm is designed to estimate the low-rank and sparse contributions. Further, we propose deep learning-based parameter tuning of the algorithm (i.e., algorithm unfolding) to improve the accuracy and the speed of convergence of the algorithm. Our numerical results show that the proposed approach outperforms the conventional approaches in terms of mean squared errors of the recovered low-rank and sparse components and the speed of convergence.

Beamspace Scene Classification Algorithm for Low-Angle Estimation in MIMO Radar

This paper discusses the issue of low-angle estimation in multiple-input, multiple-output (MIMO) radar. Due to the low amount of data transmission, storage, and computation required for beamspace super-resolution algorithms in low-angle estimation, the method has gained considerable interest in recent years. This paper develops a beamspace scene classification (BSC) algorithm to enhance the performance of low-angle estimation in MIMO radar. The proposed BSC algorithm solves an initial angle estimate and the multipath coefficient estimate using the 3D beamspace data, and further constructs the beamspace data required for transmitting and receiving sides to reduce the data dimensions. It is additionally used to provide three estimation schemes with closed-form solutions for three multipath scenes (obtaining two estimations for transmitting and receiving sides). Finally, it fuses the estimates of the two sides by the minimum variance criterion. As a result, the proposed method achieves high estimation accuracy while requiring few processing resources. Moreover, the computational complexity of the proposed algorithm is examined in this study, with the results demonstrating that the proposed method is superior for engineering applications.

Low elevation and range joint estimation method with metre wave frequency diverse array‐multiple input multiple output radar based on sparse array

Frequency diverse array-multiple input multiple output (FDA-MIMO) radar not only has the advantage of waveform diversity but also can realise azimuth-range correlation map, so it can realise high precision azimuth-range joint estimation and has the ability to distinguish different range unit targets in the same azimuth. However, the existing research mainly adopts uniform linear array (ULA) model and mainly focusses on incoherent targets. At present, the elevation-range joint estimation algorithm of metre wave FDA-MIMO radar based on ULA has the problems of sharp decline in angle-range measurement accuracy of ultra-low altitude targets and large fluctuation of elevation-range measurement accuracy with changing elevation. In order to solve the above difficult problems, starting from the perspective of reducing the influence of multipath effect by array structure, this paper innovatively combines sparse array with FDA-MIMO radar, establishes the low elevation-range joint estimation model of metre wave FDA-MIMO radar based on sparse array, and puts forward the low elevation-range joint estimation method suitable for this model, and it fills the gap of low elevation-range measurement method based on sparse array metre wave FDA-MIMO radar. Simulation experiments compare two typical sparse arrays with ULA of equal array elements, which verify the superiority of low elevation-range measurement performance of monostatic metre wave FDA-MIMO radar based on sparse array and the effectiveness of the proposed method.

A Deep Learning Based Super Resolution DoA Estimator With Single Snapshot MIMO Radar Data

In this article, we study the radar angular resolution, the capability of distinguishing multiple targets in different directions of arrivals (DoA). We present a deep learning-based super resolution DoA estimator for multiple input multiple output (MIMO) radar with single snapshot data. The estimator consists of a deep learning DoA classifier (DLDC) in the central bearing angle zone, which can simultaneously detect up to 11 targets in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$[ { - 2^\circ,\ 2^\circ } ]$</tex-math></inline-formula> with 32 virtual antenna elements, and a spatial filtering pre-rotator (SFPR) that makes the DLDC supporting any bearing angle location in a wider radar field of view (FoV). We present the DLDC-SFPR structure and provide detailed parameter setting. In the performance evaluation, the resolution of proposed method is first compared with the theoretical angular resolution limit (ARL). It is shown that the proposed estimator reaches the Chernoff ARL. The probability of resolution (PoR) is then investigated and compared with typical and state-of-the-art DoA estimators in various conditions. The numerical results show that the DLDC-SFPR achieves 0.4 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> resolution with 90% PoR to distinguish 2 targets at signal to noise ratio (SNR) of 7 dB if the targets are on-gird. In the case of any-angle, the same resolution and probability are achieved at SNR = 12 dB. The proposed method is robust to multi-target concurrent cases, and outperforms the existing reported approaches. Two procedures, uniform scanning and progressive scanning, are proposed for wider FoV applications.

W‐Band 76–81 GHz Millimeter‐Wave Comb‐Line Array for Automotive Short Range Radar

AbstractIn this paper, we design a W‐band millimeter‐wave antenna for the band of 76–81 GHz for frequency modulated continuous wave (FMCW) multiple input multiple output radar systems in automotive applications. It exhibits radiation patterns that possess narrow beamwidths with minimal beam squint in the elevation plane within the band for detection of vehicles and pedestrians in the forward direction instead of objects on the pavement and above the horizon, as well as a wide beamwidth in the horizontal plane, all as required by automotive radar antennas. A four‐port array of comb‐line antennas with diagonally tilted stubs is herein designed, the latter feature for mitigation of interruption from oncoming vehicles traveling along the opposite direction. In order to suppress the mutual interference between multiple antennas in the array, an electromagnetic band gap (EBG) structure is designed to enhance the isolation between ports. A prototype of the design was manufactured and tested, yielding results that satisfy the design requirements.

Quantisation‐based target detection for cooperative multiple input multiple output radar and communications with multiple stations

Target detection is investigated for the emerging integrated radar and communication (IRC) systems. Consider the case, where multiple radar and communication stations are physically separated, possibly widely spaced, while working in a cooperative way. To utilise all available information for a final decision making, the radar stations that receive signals send their local measurements to a fusion centre (FC) through a wireless channel, say the backhaul network. For the purpose of decreasing communication traffic, the local measurements are quantised and then transferred to the FC. Three strategies for the quantisation and fusion are considered, where for the first method, local sensors quantise their test statistics, which are linearly fused at the FC (quantising test statistics and linear combination fusion), for the second method, the test statistics are quantised at the local sensors and optimally fused at the FC (quantising test statistics and optimal fusion), and for the third method, the received signals are quantizsd at the local sensors and optimally fused at the FC (quantising received signals and optimal fusion). The detection probability for each method is derived for the cooperative IRC system and compared with that of the non-cooperative counterpart. It is proved that there is a detection performance gain through cooperation between the radar and communication systems and the cooperative IRC system can achieve the same performance as the non-cooperative system with a fewer number of quantisation bits. The correctness of theoretical analysis is verified by simulations.

Adaptive Time-Space Resource and Waveform Control for Collocated MIMO Radar with Simultaneous Multi-Beam

Collocated multiple input multiple output (MIMO) radar, which has agile multi-beam working mode, can offer enhanced multiple targets tracking (MTT) ability. In detail, it can illuminate different targets simultaneously with multi-beam or one wide beam among multi-beam, providing greater degree of freedom in system resource control. An adaptive time-space resource and waveform control optimization model for the collocated MIMO radar with simultaneous multi-beam is proposed in this paper. The aim of the proposed scheme is to improve the overall tracking accuracy and meanwhile minimize the resource consumption under the guarantee of effective targets detection. A resource and waveform control algorithm which integrates the genetic algorithm (GA) is proposed to solve the optimization problem. The optimal transmitting waveform parameters, system sampling period, sub-array number, binary radar tracking parameter <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\chi_i(t_k)$</tex> , transmitting energy and multi-beam direction vector combination are chosen adaptively, where the first one realizes the waveform control and the latter five realize the time-space resource allocation. Simulation results demonstrate the effectiveness of the proposed control method.

MIMO Radar Hardware Acceleration with Enhanced Resolution

This paper proposes a method for accelerating an enhanced resolution 3D (range, velocity, and azimuth) Multiple Input Multiple Output (MIMO) radar on a Graphics Processing Unit (GPU). Implementation of MIMO radars and the investigation of their performance are of interest to the research community. However, the MIMO mode of operation increases the computational requirements of the radar system and seldom permits real-time operation without performance compromises. Current methods for achieving reasonable frame rates include reducing the scope of the radar (i.e., limiting the number of dimensions, the field of view, or the ranges of interest), choosing efficient but coarse algorithms (i.e., the FFT for range, velocity, and bearing estimation), or offloading the computation on task specific hardware, DSP, or FPGA. The proposed framework enables real-time operation of the MIMO radar by performing the signal processing on a GPU without compromising the radar coverage, while replacing the widely used 3D FFT with an enhanced resolution alternative. The proposed framework is tested on a Frequency Modulated Continuous Wave (FMCW) MIMO radar using 8 transmitters, 8 receivers, and having a Coherent Processing Interval (CPI) of 256 chirps. The parallel implementation of the enhanced resolution signal processing yields an acceleration of 453.6x when compared to sequential execution on CPU.

Comparison of massively parallel algorithms on graphics processing unit for MIMO radar

This paper proposes a method for accelerating an enhanced resolution 3D Multiple Input Multiple Output (MIMO) radar on a Graphics Processing Unit (GPU). Due to the size of the data required for range, bearing, and doppler processing, computations for the MIMO radar are extensive and seldom permit real-time operation without performance compromises. Current methods for achieving reasonable frame rates include reducing the scope of the radar (i.e., limiting the number of dimensions, the field of view, or the ranges of interest), choosing efficient but coarse algorithms (i.e., the FFT for range, velocity, and bearing estimation), or offloading the computations on task specific hardware, DSP, or FPGA. The proposed framework enables real-time operation of the MIMO radar by performing the signal processing on a GPU without compromising the radar coverage, while replacing the widely used 3D FFT with an enhanced resolution alternative. This paper compares the execution times of the various algorithms when performed on a Central Processing Unit (CPU), and when performed on the GPU.

Robust Reweighted ℓ2,1-Norm Based Approach for DOA Estimation in MIMO Radar Under Array Sensor Failures

To alleviate the direction of arrival (DOA) estimation performance degradation under array sensor failures in multiple input multiple output (MIMO) radar, joint DOA estimation and missing data recovery is dealt with by a reweighted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{2,1}$ </tex-math></inline-formula> -norm framework. Firstly, we employ a technique based on singular value decomposition (SVD) to reduce the dimension of the array data matrix and the sensitivity to observation noise. Then, a novel reweighted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{2,1}$ </tex-math></inline-formula> -norm minimization framework is introduced to improve the robustness against model mismatch caused by sensor failures, which incorporates the recovery of the complete array data matrix into the sparse reconstruction and constructs the weight matrix for reweighting the mixed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{2,1}$ </tex-math></inline-formula> -norm minimization. Thereafter, the alternative direction method of multipliers (ADMM) under the augmented Lagrange multiplier (ALM) framework is applied to solve this multivariable optimization problem with multiple constraints, in which the sparse solution is iteratively obtained along with the recovery of the complete array data matrix. Finally, the estimation of DOAs can be achieved by finding the non-zero rows in the reconstructed sparse matrix. Simulations demonstrate that the proposed method has better estimation performance than the existing methods for handling array sensor failures and exhibits robustness in the face of a wide range of faulty sensor number.

Three‐dimensional decoupling imaging method for wideband two‐dimensional multiple‐input‐multiple‐output radar

Wideband two-dimensional multiple input multiple output (MIMO) radar can be used for target three-dimensional (3-D) imaging. As the echo of wideband MIMO radar is derived from both time and spatial sampling, the time-frequency coupling is contained, resulting in imaging defocussing. To solve this problem, a time-frequency decoupling algorithm based on matrix enhancement and matrix pencil (MEMP) is proposed to achieve high-resolution 3-D imaging. By combining the enhanced matrix constructed via MEMP with the modern spectral estimation theory, the time-frequency coupling term is regarded as a space rotation factor and is eliminated in the process of target position estimation. Simulation results show that the proposed algorithm can reduce the negative influence of time-frequency coupling and improve the imaging accuracy of wideband two-dimensional MIMO radar.

A New Method for SIAR Transmitting Waveforms to Reduce the Range-Angle Coupling

Synthetic impulse and aperture radar (SIAR) is an orthogonal multiple input multiple output radar. It has several advantages, including the ability to detect multiple targets, Antiradiated Missiles (ARM), and stealth targets. Despite its four-dimensional (4-D) nature, it exhibits a strong coupling between range and angle, which dictates how well SIAR performs in the range-angle domain. Not only does the SIAR use frequency codes to separate the antennas, but also to ensure orthogonality. They can, however, mitigate the strong coupling between range and angle. Pulse-to-pulse frequency code agility is a technique for suppressing the SIAR radar's sidelobes. In this letter, we propose a method for reducing the coupling between range and angle by combining sequential positive, negative and random code with the dot product. The idea of coupling reduction presented in this letter is based on the ratio of each antenna position and frequency codes, which is referred to $\alpha $ k , and multiple $\alpha $ k in an array provides new advantages for target detection. This technique has the potential to significantly reduce the sidelobe levels.

A new method for Synthetic Impulse and Aperture Radar transmitting waveform design based on non‐sequential positive, negative and random carrier frequency coding with dot product approach

Synthetic Impulse and Aperture Radar (SIAR) is a kind of Multiple Input Multiple Output (MIMO) radar. It is a four-dimensional radar that has advantages against stealth targets, multi targets, anti-radiated missile, reconnaissance and interference, as well as valuable features in high range resolution and high detection probability. SIAR uses special frequency coding separately in each antenna. This code has an essential function in SIAR, such as separating frequency bandwidth, satisfying orthogonality in transmitted signals, suppressing range-angle and range-Doppler frequency coupling. Pulse-to-pulse frequency code agility is a kind of radar signal coding that is used to suppress the sidelobes in range-Doppler frequency. In this study, a design method is suggested to transmit waveform based on non-sequential positive, negative and random carrier frequency coding with the dot product approach in pulse-to-pulse frequency code agility, to reduce sidelobes in range-Doppler frequency coupling. The simulation results showed that this method could effectively remove the strong coupling between range-Doppler frequency. Removing coupling in the range-Doppler frequency decreases the sidelobes.

Robust Range Ambiguous Deceptive Target Suppression Based on Covariance Matrix Reconstruction

When the deceptive targets are in the ambiguious range bin but are received at the same range gate with the desired target by the array, the traditional multiple-input multiple-output (MIMO) radar is not able to discriminate between them. Based on the unique range-dependent beampattern of the frequency diverse array (FDA)-MIMO radar, we propose a novel robust mainlobe deceptive target suppression method based on covariance matrix reconstruction to form nulls at the frequency points of the transmit–receive domain where deceptive targets are located. First, the proposed method collects the deceptive targets and noise information in the transmit–receive frequency domain to reconstruct the jammer-noise covariance matrix (JNCM). Then, the covariance matrix of the desired target is constructed in the desired target region, which is assumed to already be known. The transmit–receive steering vector (SV) of the desired target is estimated to be the dominant eigenvector of the desired target covariance matrix. Finally, the weighting vector of the receive beamformer is calculated by combining the reconstructed JNCM and the estimated desired target SV. By implementing the weighting vector at the receiving end, the deceptive targets can be effectively suppressed. The simulation results demonstrate that the proposed method is robust to SV mismatches and provides a signal-to-jamming-plus-noise ratio (SJNR) output that is close to the optimal.

Solving Manifold Ambiguity by Sliding the Array in MIMO Radar

Arbitrary sparse array could extend the array aperture. And the position of sparse array elements has little constraint. But, the manifold ambiguity of sparse array will cause ambiguity angle estimation. In order to avoid manifold ambiguity in Multiple Input Multiple Output (MIMO) radar with sparse array, a new solving ambiguity method is proposed based on sliding the array. By employing reduced multiple signal classical algorithm(RE-MUSIC), the true direction of departure (DOD) and direction of arrival (DOA) are obtained. The main idea of the proposed method is that get DODs and DOAs separately by RE-MUSIC algorithm. When transmitter exists the ambiguity, we compare the RE-MUSIC spectrum of pre-sliding transmitting array with the RE-MUSIC spectrum of post-sliding transmitting array to obtain the true DODs. Then we obtain the true DOAs by the RE-MUSIC spectrum of receiving array. It is similar to receiver exists the ambiguity. Simulation results verify the validity of the proposed method.