Multiple-input Multiple-output Radar Target Research Articles

Joint DOD and DOA Estimation of Coherent Targets for Coprime MIMO Radar

Recently, coprime arrays have attracted lots of interest due to their ability of providing enhanced degrees-of-freedom and reduced mutual coupling effect compared to conventional uniform linear arrays. Benefitting from these excellent properties, coprime multiple-input multiple-output (MIMO) radar has been recently suggested for improving parameter identifiability and target detection. In this paper, we address the problem of joint direction-of-departure (DOD) and direction-of-arrival (DOA) estimation of coherent targets in coprime MIMO radar. First of all, we establish an extended virtual uniform rectangular array (URA) by performing array interpolation on the transmit and receive arrays of coprime MIMO radar. Subsequently, we derive a low-rank block Hankel matrix that is constructed using the correlation information of the virtual URA expected outputs, and utilize the matched filter outputs to recover the block Hankel matrix by solving a low-rank structured matrix completion problem. Finally, we estimate the DODs and DOAs of coherent targets by applying the modified matrix pencil method on the recovered low-rank matrix. We also derive the Cramér-Rao bounds with closed-form expressions for DOD and DOA estimation of coherent targets using coprime MIMO radar. The proposed algorithm can identify multiple coherent targets and achieve parameter automatic pairing. Numerical results demonstrate the superiority of the proposed algorithm over several existing approaches when handling coherent targets.

$\ell _{0}$-Norm Minimization Based Robust Matrix Completion Approach for MIMO Radar Target Localization

In this paper, we propose a robust matrix completion approach based on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{0}$</tex-math></inline-formula> -norm minimization for target localization in sub-Nyquist sampled multiple-input multiple-output (MIMO) radar. Owing to the low-rank property of the noise-free MIMO radar transmit matrix, our approach is able to recover the missing data and resist impulsive noise from the receive matrix. We adopt proximal block coordinate descent and adaptive penalty parameter adjustment by complex Laplacian kernel and normalized median absolute deviation. We analyze the resultant algorithm convergence and computational complexity, and demonstrate through simulations that it outperforms existing methods in terms of pseudospectrum, mean square error, and target detection probability in non-Gaussian impulsive noise, even for the full sampling schemes. While in the presence of Gaussian noise, our approach performs comparably with other sub-Nyquist methods.

Localization and tracking of multiple fast moving targets in bistatic MIMO radar

In this paper, we propose a maximum likelihood (ML)-based scheme to accurately localize and track multiple fast-moving targets in multiple-input multiple-output (MIMO) radar systems. For the problem of tracking, the motion of the target will cause a spread spectrum effect, which broadens the beam width and skews the beam steering, rendering accurately localizing and tracking the fast moving targets extremely challenging. To tackle this problem, we use a locally linear model to approximate the real motion states of the target and define a vector to describe its motion state. As such, the motion states of targets at adjacent times can be accurately inferred within a short term based on this approximation model. To incorporate the effect of target motion when performing tracking, we estimate the motion state vector at a reference time from a batch of snapshots by using an ML estimator, instead of estimating the time-varying angular parameters directly as usual. Then the motion state vectors at other times can be inferred based on the estimated one by using the locally linear approximation model. The Cramér-Rao lower bound (CRLB) of the estimated parameters are also derived for performance evaluation. Numerical results validate the performance improvement of the proposed scheme compared to the benchmarks.

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.

Joint detection threshold adjustment and power allocation strategy for cognitive MIMO radar target tracking

Co-located multiple-input multiple-output (MIMO) radar can track multiple targets in the simultaneous multi-beam mode, where the defocused beams are transmitted to illuminate the whole surveillance space, while focused beams are received to attain a high Doppler resolution. A joint detection threshold adjustment and power allocation (JTAPA) strategy for this mode is proposed. The mechanism of our strategy is to improve the tracking accuracy in the clutter zone by exploiting prior target information, feedback from the filter, to facilitate transmitter and detector operation. The predicted conditional-Cramér-Rao lower bound (PC-CRLB), which provides a much tighter bound for the estimation error than the standard Bayesian CRLB (BCRLB), is derived, normalized and used as the optimization criterion. The Bayesian detection framework is embedded in the derivation to connect the detector with the filter. The optimization model is then established with bounding the transmit power and predetection threshold within intervals. It is shown that the objective function is nonlinear and nonconvex, and a flexible proximal alternating direction method of multipliers (ADMM)-based solver is proposed for solving the optimization model. Simulation results show the effectiveness of the JTAPA strategy, compared with the strategies adopting the tunable detection threshold and/or optimal power allocation (PA). In addition, the results imply that the target distance and the target radar cross section are two main factors that influence the JTAPA results.

Co-Located MIMO Radar Target Detection in Cluttered and Noisy Environment Based on 2D Block Sparse Recovery

In the co-located multiple input multiple output (MIMO) radar, the matched filter receiver suffers from high range sidelobes, since the waveforms originating from different transmitters cannot be fully orthogonal. Traditional sparse recovery based receivers can suppress the sidelobes by assuming the sparsity of range profiles. However, in most practical situations, the range profile is not sparse due to the presence of clutter. In this paper, an effective co-located MIMO radar target detection algorithm is proposed based on modified smoothed-l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> norm sparse recovery, by exploiting the 2D block sparsity of the range-Doppler profiles of targets corresponding to different transmit-receive pairs. The proposed method can enhance the target detection performance in co-located MIMO radar system, especially for those weak targets which may be overwhelmed by high-level sidelobes of strong targets and clutters, without sacrificing computational efficiency. With a well-designed regularization approach and an adaptive regularization parameter update technique, the proposed method is also robust in noisy environment. Simulation and experimental results validate the effectiveness of the proposed method.

Robust MIMO radar target localization based on lagrange programming neural network

In a multiple-input multiple-output (MIMO) radar system, there are a number of transmitters and receivers. We can use a set of range measurements from MIMO system to locate a target. Each range measurement is the sum of the transmitter-to-target distance and target-to-receiver distance, which corresponds to elliptic localization. This paper addresses the MIMO radar target localization problem with possibly outlier measurements. We formulate the problem via non-smooth constrained optimization with an ℓ1-norm objective function, which is non-differentiable, and the Lagrange programming neural network (LPNN) is adopted as the solver. As the LPNN framework cannot handle non-differentiable objective functions, we utilize two techniques, namely, approximation of the ℓ1-norm and locally competitive algorithm, to develop two LPNN based algorithms. Moreover, the stability of the LPNN-based algorithms is studied. Simulation results demonstrate that the proposed algorithms outperform two state-of-the-art algorithms.

Computationally efficient method for joint DOD and DOA estimation of coherent targets in MIMO radar

This paper proposes a method for estimating the joint direction of departure (DOD) and direction of arrival (DOA) of coherent targets in multiple-input multiple-output (MIMO) radar with electromagnetic vector sensors. The method does not require any additional processing for both decorrelating the coherent targets and pairing the DOD and DOA, and exhibits real-valued operations. Compared to the existing polarization smoothing and unitary ESPRIT, the proposed method shows improved performance which is validated by the simulation results.

Distributed MIMO radar target altitude estimation for ground‐based systems

This study presents novel target altitude estimation schemes optimised for ground-based multiple-input multiple-output (MIMO) radars with widely separated antennae, where the antennae are located in a plane so that the altitudes of the antennae are close together. For the ground-based systems, existing target localisation methods based on linear least squares such as least square (LS) and bistatic range measurement (BRM) suffer from performance degradation in target altitude estimation and eventually fail in altitude localisation for a perfect ground-based system where the altitude of antennae are the same. The authors propose three target altitude estimation schemes based on linear least squares for the ground-based systems resolving the drawback of existing algorithms for the ground-based systems; extended BRM approach, which estimates the square of target altitude from the auxiliary parameters produced by BRM algorithm, ellipsoid fitting approach, which directly estimates the square of target altitude with other target position information, and extended ellipsoid fitting approach, which utilise the estimation results of ellipsoid fitting approach to improve the target altitude estimation performance. Performance improvement of the proposed algorithms is analysed in terms of mean squared error and simulation results verify the performance.

Joint beam and waveform selection for the MIMO radar target tracking

Abstract The simultaneous orthogonal multi-beam transmitted by a collocated multiple-input multiple-output (MIMO) radar is very effective in multi-target tracking. Since this working mode can meet the requirement of low possibility of interception; meanwhile, gain higher sensitivity and resolution. Aiming at the resource management in such a scenario, a joint beam, power and waveform selection strategy is put forward. The optimization model is established subject to a certain number of beams, power budget and waveform parameters. The criterion, which is predicted by the Kalman recursive equation, is minimizing the posterior estimate errors of multiple targets in worst cases simultaneously. Thereby, the suitable number of utilized beams, the allocated power as well as the waveform parameter in each beam for the current time epoch can be adaptively turned according to the estimate errors from the previous time epoch. We then fully demonstrate such a non-convex problem can be transformed into several convex problems. As such, the solution can be provided efficiently to meet the real-time demand. Furthermore, through obtaining the posterior estimate error by the square-root cubature Kalman filter, prediction and then adapts the three resources for next time epoch, a closed loop feedback allocation scheme is established. The simulation results show that the proposed algorithm can significantly improve the tracking performance compared with the uniform and random resource allocation strategies.

DOA Estimation for Multiple Targets in MIMO Radar with Nonorthogonal Signals

This paper addresses the direction of arrival (DOA) estimation problem in the colocated multiple-input multiple-output (MIMO) radar with nonorthogonal signals. The maximum number of targets that can be estimated is theoretically derived as rankRsN, where N denotes the number of receiving antennas and Rs is the cross-correlation matrix of the transmitted signals. Therefore, with the rank-deficient cross-correlation matrix, the maximum number that can be estimated is less than the radar with orthogonal signals. Then, a multiple signal classification- (MUSIC-) based algorithm is given for the nonorthogonal signals. Furthermore, the DOA estimation performance is also theoretically analyzed by the Carmér-Rao lower bound. Simulation results show that the nonorthogonality degrades the DOA estimation performance only in the scenario with the rank-deficient cross-correlation matrix.

Performance Analysis of Locally Most Powerful Invariant Test for Sphericity of Gaussian Vectors in Coherent MIMO Radar

Locally most powerful invariant test (LMPIT) for sphericity of Gaussian vectors has been derived by Ramirez et al. Nevertheless, the decision threshold of the LMPIT is not accurate and its detection performance has not yet been addressed. In this paper, the LMPIT is performed for target detection in multiple-input multiple-output (MIMO) radar, and its theoretical decision threshold as well as detection probability are accurately determined. Utilizing asymptotic expansion approach, we calculate the asymptotic null distribution as a function of central chi-square distributions, resulting in precise closed-form formula for thresholding. On the other hand, the nonnull distribution is approximated by weighted sum of noncentral chi-square distributions and Gamma distribution for close and far hypotheses, respectively. This enables us to derive a closed-form formula to precisely evaluate the detection power of the LMPIT. Numerical results demonstrate that our theoretical computations are very accurate in determining the decision threshold and predicting the behaviors of the LMPIT. Moreover, the superiority of the LMPIT for MIMO radar target detection over state-of-the-art methods is demonstrated for spatially colored but temporarily white noise.

Fast 3D Parameters Estimation of Targets in Bistatic MIMO Radar Based on Sparse Signal Reconstruction

In this paper, three parameters of targets in bistatic multiple-input multiple-output (MIMO) radar are estimated based on sparse signal reconstruction. First, 2D scanning space in bistatic MIMO radar is divided into dense meshes, and a sparse signal model of multiple measurement vectors is constructed. Second, a constrained objective optimization function is established by using traditional $l2$ norm and is converted to an unconstrained objective optimization function. A conjugate gradient method is utilized to solve the inverse operation of a large-scale matrix and to avoid singular problems during sparse iterations. Even in 2D dense meshes of $90\times 90$ , the speed of convergence can be accelerated. The direction of arrival, the direction of departure, and the reflection coefficient of multiple targets in bistatic MIMO radar are estimated, and the 3D parameters of each target automatically match. Simulation results verify the validity and fast convergence of the proposed method.

MIMO radar target localization and performance evaluation under SIRP clutter

Multiple-input multiple-output (MIMO) radar has become a thriving subject of research during the past decades. In the MIMO radar context, it is sometimes more accurate to model the radar clutter as a non-Gaussian process, more specifically, by using the spherically invariant random process (SIRP) model. In this paper, we focus on the estimation and performance analysis of the angular spacing between two targets for the MIMO radar under the SIRP clutter. First, we propose an iterative maximum likelihood as well as an iterative maximum a posteriori estimator, for the target's spacing parameter estimation in the SIRP clutter context. Then we derive and compare various Cramér–Rao-like bounds (CRLBs) for performance assessment. Finally, we address the problem of target resolvability by using the concept of angular resolution limit (ARL), and derive an analytical, closed-form expression of the ARL based on Smith's criterion, between two closely spaced targets in a MIMO radar context under SIRP clutter. For this aim we also obtain the non-matrix, closed-form expressions for each of the CRLBs. Finally, we provide numerical simulations to assess the performance of the proposed algorithms, the validity of the derived ARL expression, and to reveal the ARL's insightful properties.

Numerical and experimental studies of target detection with MIMO radar

Spatially diverse multiple-input multiple-output (MIMO) radar systems combine multistatic measurements of the target under view into a single detection algorithm and are thereby expected to alleviate the effects of fading on target radar cross sections (RCS) as the angle of observation is varied. Previous analytical studies of target detection for this case have shown that MIMO radar detection performance can exceed that of the corresponding phased array radar if both sufficient spatial diversity and signal-to-noise ratio (SNR) are achieved. These results have been based on a statistical model for the multistatic RCS of the target that is similar to the traditional Swerling models of the monostatic RCS. The degree to which these results are applicable to specific target geometries therefore remains uncertain. To address this issue, two studies of MIMO radar target detection incorporating realistic RCS properties for specific target geometries were performed. The first study utilized a numerical method to compute the multistatic RCS of a helicopter-like target observed at center frequency 200 MHz, while the second involved radar measurements of an unmanned aerial vehicle (UAV) target at 2.75 and 4.5 GHz. MIMO radar configurations having two transmitters and either three (for the radar measurements) or four (numerical simulations) receivers were used. In both cases, multistatic received fields were combined with regulated thermal noise levels in postprocessing to study target detection performance. Because in general the azimuthal orientation of a specific target with respect to the radar is uncertain, the detection performance results shown are averaged over the azimuthal orientation angle of the target. The average over target orientation can also be interpreted as similar to an average over “trials” of a statistical target description, enabling comparisons of field properties averaged over target orientation with similar ensemble averages from the statistical models of the literature. Although detection performance curves for the specific targets considered are not identical to those predicted analytically by the statistical target model, results for these targets confirm that the MIMO radar system can achieve enhanced detection performance as compared with the corresponding phased array radar system.

Joint Transmission and Reception Diversity Smoothing for Direction Finding of Coherent Targets in MIMO Radar

The direction estimation problem of coherent targets in multiple-input multiple-output (MIMO) radar systems is studied and a scheme with joint transmission and reception diversity smoothing is proposed. When both the transmitting and receiving antenna arrays are located closely in space, the new approach leads to much more available covariance matrices for spatial smoothing to decorrelate the coherent signals. As a result, a better estimation performance is achieved compared to the existing transmission diversity smoothing (TDS) method. It can also identify more coherent targets when sparse antenna arrays are employed. On the other hand, the proposed approach can be applied to joint direction of arrival (DOA) and direction of departure (DOD) estimation using existing direction estimation algorithms when the transmit and receive arrays are separated far away from each other (i.e. the bistatic case). Two specific methods are proposed under the scheme, one is based on forward-only (FO) spatial smoothing and one is based on forward-backward (FB) processing. Due to the increased number of covariance matrices for spatial smoothing, a further improved performance is achieved by the FB-based one.

Three-Dimensional Extensions of Resolvability of Two Point Targets in MIMO Radar Using a Hypothesis Test Formulation

Multiple-input multiple-output (MIMO) radar has been extensively studied in recent years, due to its outstanding performance in various aspects. Resolution and estimation of the targets’ parameters are central capabilities of a MIMO radar system. In this paper, the resolution performance of MIMO radar in terms of the three-dimensional statistical resolution limit (3D SRL) is investigated, where three-dimension refers to direction of arrival (DOA), direction of departure (DOD), and Doppler frequency of a target. The scenario of two closely spaced targets corrupted by broadband noise with/without clutter sources is considered and the closed-form expression of the resolution threshold, which is the minimum signal-to-noise ratio (SNR) required for a given resolution performance, is derived using a generalized likelihood ratio test (GLRT) formulation. Finally, both theoretical and numerical analyses are provided for some insights regarding the resolution limit.

Target Localization and Tracking in Noncoherent Multiple-Input Multiple-Output Radar Systems

For a noncoherent multiple-input multiple-output (MIMO) radar system, the maximum likelihood estimator (MLE) of the target location and velocity, as well as the corresponding Cramer-Rao lower bound (CRLB) matrix, is derived. MIMO radar's potential in localization and tracking performance is demonstrated by adopting simple Gaussian pulse waveforms. Due to the short duration of the Gaussian pulses, a very high localization performance can be achieved, even when the matched filter ignores the Doppler effect by matching to zero Doppler shift. This leads to significantly reduced complexities for the matched filter and the MLE. Further, two interactive signal processing and tracking algorithms, based on the Kalman filter and the particle filter (PF), respectively, are proposed for noncoherent MIMO radar target tracking. For a system with a large number of transmit/receive elements and a high signal-to-noise ratio (SNR) value, the Kalman filter (KF) is a good choice; while for a system with a small number of elements and a low SNR value, the PF outperforms the KF significantly. In both methods, the tracker provides predictive information regarding the target location, so that the matched filter can match to the most probable target locations, reducing the complexity of the matched filter and improving the tracking performance. Since tracking is performed without detection, the presented approach can be deemed as a track-before-detect approach. It is demonstrated through simulations that the noncoherent MIMO radar provides significant tracking performance improvement over a monostatic phased array radar with high range and azimuth resolutions. Further, the effects of coherent integration of pulses are investigated for both the phased array radar and a hybrid MIMO radar, where only the pulses transmitted and received by colocated transceivers are coherently integrated and the other pulses are combined noncoherently. It is shown that the hybrid MIMO radar achieves significant tracking performance improvement when compared with the phased array radar, by using the extra Doppler information obtained through coherent pulse integration.

Game theoretic design for polarimetric MIMO radar target detection

Polarimetric radar systems allow the flexibility of transmitting arbitrarily polarized waveforms that match the scattering profiles of the target. Since different types of targets have varying profiles, the advantages of a polarimetric radar system can fully be exploited only when the type of target is accurately estimated. However, accurate estimation requires a significant amount of training data, which can be expensive. We propose a polarimetric design scheme for distributed multiple input multiple output (MIMO) radar target detection. We formulate the selection of transmit polarizations using a game theoretic framework by examining the impact of all possible transmit schemes on the detection performance with different available target profiles (see also Gogineni and Nehorai, 2011 [1]). This approach does not require training data, and we show a significant performance improvement due to the polarimetric design. Other radar design problems can also be solved using this game theoretic approach.

MIMO Radar Target Detection Performance Analysis with Unknown Parameters

Motivated by the development of Multiple-Input Multiple-Output (MIMO) communication, MIMO radar has drawn considerable attention. While, to design of MIMO radar detector, transmitting signal power and noise are usually assumed known in advance, but in practice we may need to estimate the transmitting signal power and noise first. In this paper, we introduce MIMO radar target performance analysis with unknown parameters. First transmitting signal energy is estimated by Maximum likelihood Estimation(MLE) when multipath satisfy special diversity condition and multipath has low rank. Then the detector in the Neyman-Pearson is developed and analyzed with estimated parameters. The simulation results show that the performance with unknown parameters is approximate to the detector with known parameters. The method proposed in this paper can be used to design the MIMO radar detectors with unknown parameters.