Multi-input Multi-output Radar Research Articles

Joint Angle and Array Gain-Phase Errors Estimation Using PM-like Algorithm for Bistatic MIMO Radar

This paper discusses the problem of the direction of departure (DOD) and the direction of arrival (DOA) estimation for multi-input multi-output (MIMO) radar with array gain-phase errors. In this paper, we propose a propagator method (PM)-like algorithm for joint angle and array gain-phase errors estimation in MIMO radar. The proposed method not only yields automatically paired estimates of the angles and gain-phase errors but also has much better gain-phase errors estimation performance than the estimation of signal parameters via rotational invariance techniques (ESPRIT)-like algorithm; this has higher computational cost than the proposed algorithm. Furthermore, the proposed algorithm has angle estimation performance very close to ESPRIT-like algorithm. We also derive the Cramer–Rao bound (CRB) for MIMO radar with array gain-phase errors. Simulation results present the usefulness of our approach.

Exact theoretical performance analysis of optimum detector in statistical multi-input multi-output radars

This study is concerned with the performance analysis of detection problem in statistical multiple-input multiple-output radars for Gaussian interference. This subject has been addressed in some publications for such special cases as white Gaussian noise and orthogonal transmission. However, theoretical performance analysis of optimum detector for general case including coloured Gaussian interference and arbitrary transmission signal has not been reported yet. In the present study, after developing the optimum detector for a general case, exact closed-form expressions are derived for the probability of detection and false alarm. As the derived expressions have complicated form, their interpretation is not tractable in the general case. Therefore lower and upper Chernoff bounds are obtained to provide better insight into the detector performance. Furthermore, the effect of various parameters on the detector performance is investigated by Monte-Carlo simulations. Numerical analysis shows a high degree of consistency between the theoretical and Monte-Carlo simulation results.

Comparison of the estimation performance of coherent and non-coherent ambiguity functions for an ultrawideband multi-input–multi-output noise radar

The authors start by analysing the mean square error (MSE) of target velocity and location estimation in ultrawideband (UWB) multi-input–multi-output (MIMO) noise radar. In the authors' system architecture, transmit antennas are illuminated by UWB-independent noise waveforms to meet the requirement of MIMO spatial diversity. The ambiguity function (AF) formulation is applied to implement the estimations. Since the maximum value of the AF is attained when the time-delay and Doppler stretch of replica signals are exactly matched with the ones corresponding to the reflections, this estimation is also a peak localisation problem. When noise is added, the peak may be located in a different place causing error. In this study, the authors formulate probability density functions (pdfs) to approximate the distributions of coherent and non-coherent ambiguity functions (CAFs and NCAFs) that are then applied to analyse MSE of their estimates. The pdfs are also applied to analyse the detection performance based on different AF approaches. Based on the analyses, the authors demonstrate that the NCAF-based estimation is a better approach in spatial diversity MIMO radars.

Weiss–Weinstein bound for MIMO radar with colocated linear arrays for SNR threshold prediction

Several works have suggested that a multi-input multi-output (MIMO) radar system offers improvement in terms of performance in comparison with classical phased-array radar. However, under the widely spread assumption of a uniform a priori distribution for one parameter of interest, there is no result concerning lower bounds on the mean-square error in the case of a Gaussian observation model with parameterized mean. This Fast Communication fills this lack by using the Weiss–Weinstein bound (WWB) which can be calculated under this difficult scenario. As we will show, the proposed bound for MIMO Radar with colocated linear arrays has no closed-form expression. To solve this problem, we propose a closed-form approximation that, as we will show by simulations, is close to the actual bound. This approximated bound is then analyzed for a design purpose in terms of array geometry. Simulations confirm the good ability of the proposed bound to predict the mean square error (MSE) of the maximum a posteriori (MAP) in all ranges of SNR. Particularly, the tightness of the bound to predict the SNR threshold effect is shown.

MIMO Radar Sensitivity Analysis for Target Detection

We consider the effect of imperfect separability in the received signals on the detection performance of multi-input multi-output (MIMO) radar with widely separated antennas. The mutual orthogonality among the received signals is often assumed but cannot be achieved in practice for all Doppler and delay pairs. We introduce a data model considering the correlation among the data from different transmitter-receiver pairs as unknown parameters. Based on the expectation maximization algorithm, we propose a method to estimate the target, correlation, and noise parameters. We then use the estimates of these parameters to develop a statistical decision test. Employing the asymptotic statistical characteristics and the numerical performance of the test, we analyze the sensitivity of the MIMO radar with respect to changes in the cross-correlation levels of the measurements. We demonstrate the effect of the increase in the correlation among the received signals from different transmitters on the detection performance.

Joint optimization of MIMO radar waveform and biased estimator with prior information in the presence of clutter

In this article, we consider the problem of joint optimization of multi-input multi-output (MIMO) radar waveform and biased estimator with prior information on targets of interest in the presence of signal-dependent noise. A novel constrained biased Cramer-Rao bound (CRB) based method is proposed to optimize the waveform covariance matrix (WCM) and biased estimator such that the performance of parameter estimation can be improved. Under a simplifying assumption, the resultant nonlinear optimization problem is solved resorting to a convex relaxation that belongs to the semidefinite programming (SDP) class. An optimal solution of the initial problem is then constructed through a suitable approximation to an optimal solution of the relaxed one (in a least squares (LS) sense). Numerical results show that the performance of parameter estimation can be improved considerably by the proposed method compared to uncorrelated waveforms.

Design and performance analysis of orthogonal coding signal in MIMO-SAR

Multi-input multi-output (MIMO) radar which has evident advantages in many applications is a new radar system. Applying the MIMO technique to the earth observing synthetic aperture radar (SAR) system offers effective ways for the improvement of high resolution and wide swath imaging and ground moving target indication (GMTI) systems. Designing the optimal orthogonal waveform is a crucial problem in the research on MIMO radar. First, the index definition of synthetic integrated side-lobe level ratio (ISLR) is proposed by focusing on the SAR application and considering the cross-correlation energy influences between orthogonal coding signals with the same frequency band. Second, it is theoretically demonstrated that the performance of synthetic ISLR of orthogonal coding signals with the same frequency band cannot meet the demands of SAR imaging, which has been proved by one-dimensional numerical simulation. Third, it has been shown through numerical simulation that the performance of synthetic ISLR of orthogonal coding signals still cannot be improved by dealing with the mismatched filtering. Finally, a set of orthogonal phase coding signals are designed for multiple MIMO-SAR antennas. The conclusions are verified through MIMO-SAR imaging and InSAR simulation experiments.

Two-dimensional scattering centre extraction based on multi-input multi-output radars

This article presents two-dimensional (2-D) scattering centres' parameter estimation based on linear uniform multi-input multi-output (MIMO) radars. Unlike single-input single-output (SISO) radars, MIMO radars transmit multiple detection signals at the same time. We assume that each antenna of a MIMO radar transmits signals with a set of different spread spectrum codes so as to distinguish the echoes from all antennas. Then, with spread spectrum and de-spread spectrum techniques, each antenna of a MIMO radar could distinguish its own stepped-up frequency echoes transmitted by itself. In this article, we take m sequences as spread spectrum codes and assume that the ratio between the MIMO radars array aperture and the target distance is much less than 1, then we use 2-D fast Fourier transform (FFT) and matrix extended matrix pencil (MEMP) methods to extract 2-D scattering centres' parameters after MIMO radar have received de-spread echoes. Simulations illustrate that both SISO radars and linear uniform MIMO radars using the spread spectrum technique can get similar estimation results, and the MEMP method can afford super-resolution estimation results, whereas the 2-D FFT method is more useful when the number of MIMO radar array elements are no more than the number of scattering centres.

Adaptive subspace detector for multi-input multi-output radar in the presence of steering vector mismatch

This study studies the problem of adaptive target detection by multi-input multi-output (MIMO) radar with multiple antennas at each of the receiver sites. MIMO radar has been postulated to have a number of advantages over its monostatic counterpart. However, many previous publications have not necessarily made valid assumptions in assessing performance potential. Here, the authors consider the issue of the overall MIMO radar system steering vector which should be aligned with the desired pointing direction. However, misalignment can occur because of calibration errors, receiver channel imperfections and beam pointing errors. To account for this misalignment, the steering vector is assumed to belong to a known linear subspace. At the design stage, the authors resort to a generalised likelihood ratio principle and Rao test criterion. Subsequently, MIMO versions of the subspace generalised likelihood ratio test (MIMO-SGLRT), the subspace adaptive beam-former orthogonal rejection test (MIMO-SABORT) and the subspace Rao (MIMO-SRao) detector are developed to improve the robustness of MIMO radar detection performance for the case of steering vector mismatch. The constant false alarm rate properties of the three detectors are demonstrated. Finally, the performance of the MIMO-SGLRT, MIMO-SABORT and MIMO-SRao detectors in both the matched and mismatched steering vector cases are numerically evaluated. The results show the robustness of the proposed detectors to steering vector mismatch.

Wideband MIMO Systems: Signal Design for Transmit Beampattern Synthesis

The usage of multi-input multi-output (MIMO) systems such as a MIMO radar allows the array elements to transmit different waveforms freely. This waveform diversity can lead to flexible transmit beampattern synthesis, which is useful in many applications such as radar/sonar and biomedical imaging. In the past literature most attention was paid to receive beampattern design due to the stringent constraints on waveforms in the transmit beampattern case. Recently progress has been made on MIMO transmit beampattern synthesis but mainly only for narrowband signals. In this paper we propose a new approach that can be used to efficiently synthesize MIMO waveforms in order to match a given wideband transmit beampattern, i.e., to match a transmit energy distribution in both space and frequency. The synthesized waveforms satisfy the unit-modulus or low peak-to-average power ratio (PAR) constraints that are highly desirable in practice. Several examples are provided to investigate the performance of the proposed approach.

Direction of Departure (DOD) and Direction of Arrival (DOA) Estimation in MIMO Radar with Reduced-Dimension MUSIC

This letter discusses the problem of direction of departure (DOD) and direction of arrival (DOA) estimation for multi-input multi-output (MIMO) radar, and derives a reduced-dimension multiple signal classification (MUSIC) algorithm therein. The proposed algorithm, which only requires one-dimension search, can avoid the high computational cost within two-dimension MUSIC (2D-MUSIC) algorithm. We illustrate that the algorithm has better performance ESPRIT algorithm, and has very close performance to 2D-MUSIC algorithm. Further our algorithm requires no pair matching. Simulation results verify the usefulness of our algorithm.

MIMO Radar Detection and Adaptive Design Under a Phase Synchronization Mismatch

We consider the problem of target detection for multi-input multi-output radar with widely separated antennas in the presence of a phase synchronization mismatch between the transmitter and receiver pairs. Such mismatch often occurs due to imperfect knowledge of the locations and local oscillator characteristics of the antennas. First, we introduce a data model using a von Mises distribution to represent the phase error terms. Then, we employ an expectation-maximization algorithm to estimate the error distribution parameter, target returns, and noise variance. We develop a generalized likelihood ratio test target detector using these estimates. Based on the mutual information between the radar measurements and received target returns (and hence the phase error), we propose an algorithm to adaptively distribute the total transmitted energy among the transmitters. Using Monte Carlo simulations, we demonstrate that the adaptive energy allocation, increase in the phase information, and realistic measurement modeling improve the detection performance.

Mimo radar imaging model and algorithm

Based on the array architecture of multiple transmitting/receiving antennas, Multi-Input Multi-Output (MIMO) radar provides a new mechanism for radar imaging technology. In order to explore the processing approach to this imaging mechanism, the two dimensional (2D) imaging model of MIMO radar is established first, and the spatial sampling ability is analyzed from the concept of spatial convolution of the antenna elements. The target spatial spectral filling format of MIMO radar with monochromatic transmitting signal is described. High-resolution imaging capability of MIMO radar is analyzed according to spatial spectral coverage and the corresponding imaging algorithm is presented. Finally, field imaging experiment is used to demonstrate the superior imaging performance of MIMO radar.

BLIND JOINT DOA AND DOD ESTIMATION AND IDENTIFIABILITY RESULTS FOR MIMO RADAR WITH DIFFERENT TRANSMIT / RECEIVE ARRAY MANIFOLDS

This paper derives its link of direction of arrival (DOA) and direction of departure (DOD) estimation problem for multi- input multi-output (MIMO) radar to the trilinear model. With the exploitation of this link, we present a trilinear decomposition- based blind algorithm for angle estimation in MIMO radar. After the algorithmic presentation and discussions for identiflability results, trilinear decomposition-based angle estimation under difierent array conditions has also been investigated. Our proposed algorithm works well without either spectral peak searching or pair matching, has better angle estimation performance than ESPRIT, and even supports small sampling sizes. Simulation trials testify that the merits of our algorithmic performance are in collaboration with theoretical flndings.

Adaptive detectors with diagonal loading for airborne multi-input multi-output radar

The problem of adaptive target detection for airborne multi-input multi-output (MIMO) radars with space–time receivers in the presence of Gaussian interference (including clutter and noise) is studied. Previous work has assumed the interference covariance matrix to be known. The case with unknown covariance matrix is investigated here. By exploiting the low rank property of clutter subspace, generalised likelihood ratio test detector and adaptive matched filter detector with diagonal loading are suggested to improve the detection performance of MIMO radars in limited secondary data case. The closed-form detection probabilities and false alarm probabilities of the two proposed detectors are derived and numerically evaluated. Theoretical analysis and numerical results show the advantages of the proposed detectors.

Two-sided minimum-variance distortionless response beamformer for MIMO radar

In this letter, we propose a novel two-sided minimum-variance distortionless response (TS-MVDR) beamformer applicable to multi-input multi-output (MIMO) radar systems. For this purpose, a new bi-quadratic cost function is introduced, whose minimum point can be efficiently found by a bi-iterative algorithm (BIA). The convergence of the BIA is analyzed by the well-known LaSalle invariance principle. It is shown via experimental results that the proposed TS-MVDR beamformer can achieve relatively good performance.

Temporal coherent adaptive target detection for multi-input multi-output radars in clutter

The superiority of multiple-input multiple-output (MIMO) radars over conventional phased-array radars has been recently shown in many aspects. The MIMO radars have better detection performance and can extract target information more precisely than phased-array radars. The problem of adaptive target detection using temporal coherent pulse train in the presence of nonhomogeneous clutter (different clutter statistics in receivers) is considered for MIMO radars. This problem has been formulated as a hypothesis test. For clutter with unknown statistics three adaptive decision rules have been developed using the generalised likelihood ratio test. The results show the superiority of the MIMO radars with temporal coherent processing over conventional phased arrays in the presence of clutter.

Range Compression and Waveform Optimization for MIMO Radar: A CramÉr–Rao Bound Based Study

A multi-input multi-output (MIMO) radar system, unlike standard phased-array radar, can transmit via its antennas multiple probing signals that may be correlated or uncorrelated with each other. This waveform diversity offered by MIMO radar enables superior capabilities compared with a standard phased-array radar. One of the common practices in radar has been range compression. We first address the question of ldquoto compress or not to compressrdquo by considering both the Cramer-Rao bound (CRB) and the sufficient statistic for parameter estimation. Next, we consider MIMO radar waveform optimization for parameter estimation for the general case of multiple targets in the presence of spatially colored interference and noise. We optimize the probing signal vector of a MIMO radar system by considering several design criteria, including minimizing the trace, determinant, and the largest eigenvalue of the CRB matrix. We also consider waveform optimization by minimizing the CRB of one of the target angles only or one of the target amplitudes only. Numerical examples are provided to demonstrate the effectiveness of the approaches we consider herein.

On Parameter Identifiability of MIMO Radar

A multi-input multi-output (MIMO) radar system, unlike a standard phased-array radar, can transmit multiple linearly independent probing signals via its antennas. We show herein that this waveform diversity enables the MIMO radar to significantly improve its parameter identifiability. Specifically, we show that the maximum number of targets that can be uniquely identified by the MIMO radar is up to Mt times that of its phased-array counterpart, where Mt is the number of transmit antennas.