Multiple-input Multiple-output Radar Research Articles

Adaptive Distributed Target Detection for FDA-MIMO Radar in Gaussian Clutter Without Training Data

Since frequency diverse array multiple-input multiple-output (FDA-MIMO) radar possesses additional target range information for potential performance improvement, this article studies adaptive distributed targets detection for FDA-MIMO radar, where the targets are embedded in Gaussian clutter with unknown covariance matrix. The proposed FDA-MIMO radar detection model considers also the distributed targets occupying several secondary range cells, which is different from the classic detection models in multiple-input multiple-output (MIMO) and/or phase array (PA) radars that discuss only point-like targets. By exploiting the FDA-MIMO radar framework for distributed target detection, we propose the detector through a two-step generalized likelihood ratio test criteria without the need of training data and/or a priori covariance matrix. Moreover, closed-form expressions for the probability of false alarm and detection probability are derived, respectively. The proposed detector adheres to the property of a constant false alarm rate because its probability of false alarm is not restricted by the covariance matrix. The proposed method together with all theoretical analysis are verified by numerical results.

Calibration of MIMO Radar Transmitting and Receiving Array Using Scene Object Measurement

The paper describes two simple methods allowing phase offsets to be aligned between radiators of transmitting and receiving antenna arrays in a collocated MIMO (Multiple Input Multiple Output) radar. One method uses normalization with averaging, while the second applies SVD (Singular Value Decomposition) of a measurement matrix. To calibrate phase offsets, the measurement of a calibration target at a known angular position must be done. The paper shows numerical comparison to already known method based on normalization by a single element of MIMO measurement matrix and experimental results obtained through the application of the proposed methods to measurement data.

A PARAFAC estimator for MIMO radar under direction-dependent mutual coupling

Among the various research branches in multiple-input multiple-output (MIMO) radar, Direction finding is an interesting topic and has attracted extensive concerns. However, many previous strategies are only effective to deal with scenarios without model error, such as well-calibrated sensor array, which is unrealistic in practice. This paper revisits the direction finding issue in a bistatic MIMO radar, in which the direction-dependent mutual coupling (MC) effect in both the transmitting array and the receiving array are considered. An estimator based on parallel factor (PARAFAC) decomposition is introduced. Benefit from the fact that the multidimensional structure of the tensor measurement can be explored, the proposed PARAFAC estimator can offer closed-form solution to direction finding without additional pairing calculation, so it is much more accurate and efficient than the state-of-the-art spectrum search method and the rotational invariance algorithm. The improved PARAFAC approach is mathematically analyzed in detail, Several computer trials are carried out to show the theoretical advantages.

Two-Dimensional Transceiver Beamforming for Mainlobe Jamming Suppression with FDA-MIMO Radar

With the rapid development of electronic warfare technology, the airborne electronic counter measures (ECM) system can generate mainlobe jamming using range gate pull-off (RGPO) strategy, which brings serious performance degradation of target tracking for the tracking and guidance radar. In this study, a two-dimensional transceiver beamforming approach is proposed to suppress the mainlobe jamming with frequency diverse array using multiple-input multiple-output (FDA-MIMO) radar. The mainlobe jamming signal differs from the real target echo in the joint transmit and receive domain due to the range dependence of FDA beampattern. The amplitude of RGPO signal is greater than the amplitude of real target echo. Thus, the transceiver beampattern can be designed to null out the jamming while maintaining the real target. The jamming suppression performance is studied in consideration of practical range constraint of RGPO. Simulation results are provided to verify the effectiveness of the proposed approach.

Deep MIMO radar target detector in Gaussian clutter

We focus on the target detection problem of the multiple-input multiple-output (MIMO) radar in clutter. Most of the existing MIMO radar detection works rely on the clutter models, which may limit their scopes of application. To address this issue, a deep learning based MIMO radar target detection framework is proposed in this paper, which utilises the powerful representation and discrimination capabilities of deep neural networks (DNNs) to improve the detection performance in a data-driven manner and does not require any prior information about the clutter distribution. In most classification problems, DNNs are trained with the cross entropy loss, which promotes DNNs to achieve higher accuracy. However, the main concern in the radar target detection problem is the probability of detection (PD) under a given probability of false alarm. In this framework, a novel loss is proposed to directly maximise the average PD of the DNN, or equivalently maximise the area under curve of the DNN. Under the DL-based MIMO radar target detection framework, a deep convolutional neural network (CNN) architecture is introduced, and an input feature of the received signal for the CNN based on the conventional generalised likelihood ratio test statistics is designed to further improve the detection performance. Finally, extensive simulation results are presented to validate the detection performance and the robustness of the proposed methods.

Low Elevation Estimation for High Mountain Terrain of Meter Wave MIMO Radar Based on Improved Dual Carrier Frequency

Low elevation measurement of meter wave radar is a difficult issue in array signal processing. Dual carrier frequency processing method can be adopted for high mountain terrain in the field of low elevation measurement of meter wave radar. The algorithm works very well. Therefore, the algorithm is introduced into meter wave multiple-input multiple-output (MIMO) radar, and the algorithm is improved to reduce the computational complexity. This letter mainly has two contributions. One is to transplant dual carrier frequency method from conventional array to MIMO array radar; another one is to improve the original dual carrier frequency method, which has a half of calculation amount compared with the original method. Simulation results show the effectiveness of the algorithm.

Sectorized FMCW MIMO Radar by Modular Design With Non-Uniform Sparse Arrays

Automotive radars are designed to enhance road user safety and reduce the number of accidents on public roads and there is a constant demand to improve the performance for these radars. In this paper, a novel proof-of-concept multiple-input multiple-output (MIMO) radar architecture is presented by frequency modulated continuous waveform (FMCW) transmission. For enhanced angular resolution, the radar uses a two-tier antenna setup leading to a sparse array arrangement, mainly in an effort to mitigate grating lobes and to offer different illuminations of the same scenario. Also, the experimentally verified sparse radar antenna designed for target detection at the receiver, achieves modest sidelobe levels and grating lobes well below 12 dB from the main beam maximum, whilst still maintaining competitive half-power beamwidths when compared to more conventionally spaced arrays. Moreover, the high impedance bandwidth of this receiver array and the supporting radar electronics (more than 6%) allows for the detection of targets at only a 10 cm spatial separation. The complete system is also capable of seeing a wide field-of-view (FOV) since it utilizes a network of radar modules to cover the forward −90 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> to +90 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> angular range by sectorization. In the best case, the measured radar system can resolve targets that are a distance of just <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\pm 2^\circ$</tex-math></inline-formula> apart in angular separation.

A Fast PARAFAC Algorithm for Parameter Estimation in Monostatic FDA-MIMO Radar

This paper studies the joint range and angle estimation of monostatic frequency diverse array multiple-input multiple-output (FDA-MIMO) radar and proposes a joint estimation algorithm. First, the transmit direction matrix is converted into real values by unitary transformation, and the Vandermonde-like matrix structure is used to construct an augmented output that doubles the aperture of the receive array. Then the augmented output is combined into a third-order tensor. Next, the factor matrices are initially estimated. Finally, the direction matrices are estimated utilizing parallel factor (PARAFAC) decomposition, and the range and angle are calculated by employing least square fitting. As contrasted with the classic PARAFAC method, the proposed method can estimate more targets and provide better estimation performance, and requires less computational complexity. The availability and excellence of the proposed method are reflected by numerical simulations and complexity analysis.

Dynamic Antenna Selection for Colocated MIMO Radar

Antenna distribution plays an important role for the performance gain in multiple-input–multiple-output (MIMO) radar target tracking. Since to meet the requirements of the low probability of interception, especially in a hostile environment, only a finite number of antennas can be activated at each step. This naturally leads to a performance-driven resource management problem. In this paper, a dynamic antenna selection strategy is proposed for tracking targets in colocated MIMO radar. The derived posterior Cramér–Rao lower bound (PCRLB) of joint direction-of-arrival (DOA) and Doppler estimate were chosen as the optimization criteria. Furthermore, in the deviation, the target radar cross-section (RCS) as the determining variable and the random variable are both discussed. The objective function is related to the antenna allocation and non-convex, and an efficient fast discrete particle swarm optimization (FDPSO) algorithm is proposed for the solution exploration. Additionally, a closed-loop feedback system is established, where the main idea is that the tracking information from the current time epoch is utilized to predict the PCRLB and to guide the antenna adjustment for the next time epoch. The simulation results demonstrate the performance improvement compared with the three fixed-antenna configurations. Moreover, the FDPSO can provide close-to-optimal solutions while satisfying the real-time demand.

PARAFAC Estimators for Coherent Targets in EMVS-MIMO Radar with Arbitrary Geometry

In the past few years, multiple-input multiple-output (MIMO) radar with electromagnetic vector sensor (EMVS) array, or called EMVS-MIMO radar, has attracted extensive attention in target detection. Unlike the traditional scalar sensor-based MIMO radar, an EMVS-MIMO radar can not only provides a two-dimensional (2D) direction finding of the targets but also offers 2D polarization parameter estimation, which may be important for detecting weak targets. In this paper, we investigate into multiple parameter estimations for a bistatic EMVS-MIMO radar in the presence of coherent targets, whose transmitting EMVS and receiving EMVS are placed in an arbitrary topology. Three tensor-aware spatial smoothing estimators are introduced. The core of the proposed estimators is to de-correlate the coherent targets via the spatial smoothing technique and then formulate the covariance matrix into a third-order parallel factor (PARAFAC) tensor. After the PARAFAC decomposition of the tensor, the factor matrices can be obtained. Thereafter, the 2D direction finding can be accomplished via the normalized vector cross-product technique. Finally, the 2D polarization parameter can be estimated via the least squares method. Unlike the state-of-the-art PARAFAC estimator, the proposed estimators are suitable for arbitrary sensor geometries, and they are robust to coherent targets as well as sensor position errors. In addition, they have better estimation performance than the current matrix-based estimators. Moreover, they are computationally efficient than the current subspace methods, especially in the presence of a large-scale sensor array. In addition, the proposed estimators are analyzed in detail. Numerical experiments coincide with our theoretical findings.

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.

Joint resource optimization for a distributed MIMO radar when tracking multiple targets in the presence of deception jamming

How to utilize the limited resource to achieve better tracking accuracy in a distributed multiple-input multiple-output (MIMO) radar plays a critical role under the electronic countermeasure (ECM) background. In this paper, a joint resource optimization scheme (JROS) is presented for transmit and receive station selection and power allocation in anti-deception jamming scenario. A quality-of-service (QoS) model of minimizing the gap between rejection probability of false target (RPFT) and the threshold is established, and the optimal discriminator is designed on the basis of predicted posterior Cramér–Rao lower bound (PCRLB) on the deception parameters. The binary selection variables lead to the nonconvexity of objective function. To overcome this difficulty, a three-step optimization cycle (TSOC)-based algorithm is proposed. Simulation results confirm the superiority of the proposed scheme, compared with state-of-the-art resource allocation strategies. The results also show that the stations closer to the jammer with lower normalized deception parameters are often activated and allocated to more power resource.

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.

Transmit Design for Joint MIMO Radar and Multiuser Communications With Transmit Covariance Constraint

In this paper, we consider the waveform design of a multiple-input multiple-output (MIMO) transmitter which simultaneously functions as a MIMO radar and a base station for downlink multiuser communications. In addition to a power constraint, we require the covariance of the transmit waveform be equal to a given optimal covariance for MIMO radar, to guarantee the radar performance. With this constraint, we formulate and solve the signal-to-interference-plus-noise ratio (SINR) balancing problem for multiuser transmit beamforming via convex optimization. Considering that the interference cannot be completely eliminated with this constraint, we introduce dirty paper coding (DPC) to further cancel the interference, and formulate the SINR balancing and sum rate maximization problem in the DPC regime. Although both of the two problems are non-convex, we show that they can be reformulated to convex optimizations via the Lagrange and downlink-uplink duality. In addition, we propose gradient projection based algorithms to solve the equivalent dual problem of SINR balancing, in both transmit beamforming and DPC regimes. The simulation results demonstrate significant performance improvement of DPC over transmit beamforming, and also indicate that the degrees of freedom for the communication transmitter is restricted by the rank of the covariance.

A Tensor Generalized Weighted Linear Predictor for FDA-MIMO Radar Parameter Estimation

Radar parameter estimation in terms of its range, angle and velocity plays a crucial role in many applications. Multiple-input multiple-output (MIMO) radar with frequency diverse array (FDA) is capable of resolving targets in one directional beam with different ranges and velocities (Doppler shifts). Prevalent methods obtain target parameters in a sequential manner to get rid of exhausted multi-dimensional search, but they suffer from accumulated estimation errors. To tackle this issue, a tensor generalized weighted linear predictor (TGWLP) is devised for FDA-MIMO radar parameter estimation, where the parameters are estimated in a parallel manner. Tensor modeling of multidimensional FDA-MIMO radar signal is developed, so that the joint parameter estimation is casted into multiple-pulse-group version of three-dimensional (3D) HR problem associated to the mixed Swerling model. Pulse-group diversity is exploited to obtain precise velocity estimation. In the presence of targets with some identical parameters, the final estimations of unambiguous slant range, conic angle, and radial velocity of a moving target can be easily obtained after the parallel frequency estimations. Besides, all the parameter pairing is automatically achieved, which is free of the extra burden from pairing process. Furthermore, the identifiability of the proposed joint estimator is analyzed. Finally, theoretical analysis and simulations are included to demonstrate that the proposed approach can achieve improved performance compared to the existing methods.

MIMO Waveform Design for Dual Functions of Radar and Communication With Space-Time Coding

Sharing a multiple-input multiple-output (MIMO) radar, the single platform can achieve dual functions of radar and communication (DFRC) within the same frequency spectrum, via the same transmit waveforms. In this paper, a space-time coding scheme is developed for transmit beamforming of DFRC and embedding communication information, without their cross-interference. For transmit beampattern design of DFRC, the shape approximation and integrated power approximation criteria are adopted respectively for waveforms optimization with the constant-envelope constraints of transmit waveforms and the equivalent signal in the communication direction. Based on the space-time coding scheme, the direct constellation mapping (DCM) and phase-rotation constellation mapping (PRCM) methods are proposed to embed information symbols. It turns out that the proposed space-time coding scheme for information constellation mapping can prevent missing information symbols and have better performance in bit-error rate (BER), compared to the existing information-embedding techniques. Moreover, the scheme can reduce the dependence of the communication data rate on radar pulse repetition frequency (PRF). Simulation results are presented to demonstrate the effectiveness of the proposed methods.

Target Localization in Asynchronous Distributed MIMO Radar Systems With a Cooperative Target

In general, target localization in distributed multiple-input multiple-output (MIMO) radar systems requires synchronization among the sensors, which may not be feasible when the sensors are widely separated. In this paper, the target localization problem is addressed under asynchronous distributed MIMO radar systems in the presence of clock and frequency offsets with the aid of a cooperative target, whose position and velocity are known with measurement errors. Specifically, the influence of the cooperative target position and velocity errors on the unknown target localization accuracy is analyzed through the Cramer-Rao lower bound (CRLB) derivation firstly. Then two methods, namely, the offset estimation based localization method and the offset elimination based localization method, are proposed to estimate the unknown target location. The former estimates the clock and frequency offsets firstly and then locates the unknown target based on the offset estimates, and the latter achieves the unknown target location estimate based on the differential measurements of the two targets. Through the theoretical derivation and numerical simulations, the two proposed methods are demonstrated to be approximately unbiased and can attain the CRLB under mild noise. Moreover, the simulation results show that the proposed methods enjoy higher localization accuracy than the state-of-the-art algorithms.

Power Minimization-Based Joint Resource Allocation Algorithm for Target Localization in Noncoherent Distributed MIMO Radar System

In this article, an optimization scheme integrating power allocation, bandwidth assignment and radar node selection (PABARNS) is developed to improve power utilization efficiency (PUE) of noncoherent distributed multiple-input–multiple-output (MIMO) radar system for target localization. The basis of the proposed scheme is to minimize the total transmit power subject to a predetermined target localization accuracy requirement, where the target localization accuracy is evaluated with the Crámer–Rao lower bound. Subsequently, the PABARNS scheme results in a mixed nonlinear Boolean-nonconvex optimization problem. In view of the unique feature of the optimization model, a two-step technique based on the heuristic algorithm and alternating direction method of multipliers method is presented to solve the underlying optimization problem, with merit of that it is more workable than other existing approaches when the power and bandwidth lie in any bound set. Finally, the simulation reveals the higher accuracy and lower complexity of the developed algorithm compared with the benchmarks, as well as the superiority of the PABARNS scheme compared with over other existing schemes in the view of the achievable PUE.

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.

A Nyström-Based Low-Complexity Algorithm with Improved Effective Array Aperture for Coherent DOA Estimation in Monostatic MIMO Radar

In this paper, we propose a computationally efficient algorithm with improved effective aperture for coherent angle estimation in a monostatic multiple-input multiple-output (MIMO) radar. First, the direction matrix of MIMO radar is mapped into a low-dimensional matrix of virtual uniform linear array (ULA). Then, an augmented data expansion matrix with improved effective aperture is obtained by exploiting the Vandermonde-like structure of the low-dimensional direction matrix and radar cross section (RCS) matrix to enlarge the aperture of the array. Next, a unitary transformation is used to transform the augmented matrix into a real value and the approximate signal subspace of the augmented matrix is obtained by the Nyström method, which can reduce the computational complexity. The eigenvectors of the approximate signal subspace are used to reconstruct the matrix for direct decorrelation processing. Finally, direction of arrivals (DOAs) can be estimated faster by utilizing the unitary ESPRIT algorithm since the rotation invariance of the extended reconstruction matrix still exists. The proposed algorithm has a lower total computational complexity, and the estimation accuracy is improved by utilizing real values and enlarging the array aperture for estimation. Several theoretical analyses and simulation results confirm the effectiveness and advantages of the proposed method.