Moving Target Estimation in Non-Homogeneous Clutter for MIMO Radar (original) (raw)
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Signal Processing, 2015
This paper deals with the adaptive detection of moving targets for multiple-input multipleoutput (MIMO) radar in heterogeneous clutter environments. Two new detectors based on Rao and Wald criteria are developed using an ad hoc design procedure. Precisely, we first obtain the Rao and Wald tests by assuming the known target velocity and the known structure of the clutter. Then, we modify them by performing a numerical optimization with respect to the target velocity and replacing the clutter covariance matrix with a proper estimate. With a limited number of secondary data set, a class of covariance matrix estimators, defined as the geometric barycenters of some basic covariance matrix estimates obtained from the available secondary data set, are proposed by exploiting the characteristic of the positive-definite matrix space. Finally, numerical results are presented to demonstrate the effectiveness of the proposed detectors and covariance matrix estimators.
MIMO Radar Moving Target Detection in Homogeneous Clutter
IEEE Transactions on Aerospace and Electronic Systems, 2000
A multiple-input multiple-output (MIMO) radar approach employing widely-dispersed transmit and receive antennas is studied for the detection of moving targets. The MIMO approach transmits orthogonal waveforms from the different transmit antennas so these waveforms can be separated at each receive antenna. For a moving target in colored Gaussian noise-plus-clutter, we quantify the gains from having widely-dispersed antennas that allow the overall system to "view" the target simultaneously from several different directions. The MIMO radar performance is contrasted with that of a traditional phased-array approach, which employs closely-spaced antennas for this purpose.
This paper deals with the adaptive detection of moving targets for multiple-input multiple- output (MIMO) radar in heterogeneous clutter environments. Two new detectors based on Rao andWald criteria are developed using an ad hoc design procedure. Precisely, we first obtain the Rao and Wald tests by assuming the known target velocity and the known structure of the clutter. Then, we modify them by performing a numerical optimization with respect to the target velocity and replacing the clutter covariance matrix with a proper estimate. With a limited number of secondary data set, a class of covariance matrix estimators, defined as the geometric barycenters of some basic covariance matrix estimates obtained from the available secondary data set, are proposed by exploiting the characteristic of the positive-definite matrix space. Finally, numerical results are presented to demonstrate the effectiveness of the proposed detectors and covariance matrix estimators
MIMO Radar Detection in Non-Gaussian and Heterogeneous Clutter
IEEE Journal of Selected Topics in Signal Processing, 2000
In this paper, the generalized likelihood ratio test-linear quadratic (GLRT-LQ) has been extended to the multiple-input multiple-output (MIMO) case where all transmit-receive subarrays are considered jointly as a system such that only one detection threshold is used. The GLRT-LQ detector has been derived based on the Spherically Invariant Random Vector (SIRV) model and is constant false alarm rate (CFAR) with respect to the clutter power fluctuations (also known as the texture). The new MIMO detector is then shown to be texture-CFAR as well. The theoretical performance of this new detector is first analytically derived and then validated using Monte Carlo simulations. Its detection performance is then compared to that of the well-known Optimum Gaussian Detector (OGD) under Gaussian and non-Gaussian clutter. Next, the adaptive version of the detector is investigated. The covariance matrix is estimated using the Fixed Point (FP) algorithm which enables the detector to remain texture-and matrix-CFAR. The effects of the estimation of the covariance matrix on the detection performance are also investigated.
Noncoherent MIMO Radar for Location and Velocity Estimation: More Antennas Means Better Performance
IEEE Transactions on Signal Processing, 2000
This paper presents an analysis of the joint estimation of target location and velocity using a multiple-input multiple-output (MIMO) radar employing noncoherent processing for a complex Gaussian extended target. A MIMO radar with transmit and receive antennas is considered. To provide insight, we focus on a simplified case first, assuming orthogonal waveforms, temporally and spatially white noise-plus-clutter, and independent reflection coefficients. Under these simplifying assumptions, the maximum-likelihood (ML) estimate is analyzed, and a theorem demonstrating the asymptotic consistency, large , of the ML estimate is provided. Numerical investigations, given later, indicate similar behavior for some reasonable cases violating the simplifying assumptions. In these initial investigations, we study unconstrained systems, in terms of complexity and energy, where each added transmit antenna employs a fixed energy so that the total transmitted energy is allowed to increase as we increase the number of transmit antennas. Following this, we also look at constrained systems, where the total system energy and complexity are fixed. To approximate systems of fixed complexity in an abstract way, we restrict the total number of antennas employed to be fixed. Here, we show numerical examples which indicate a preference for receive antennas, similar to MIMO communications, but where systems with multiple transmit antennas yield the smallest possible mean-square error (MSE). The joint Cramér-Rao bound (CRB) is calculated and the MSE of the ML estimate is analyzed. It is shown for some specific numerical examples that the signal-to-clutter-plus-noise ratio (SCNR) threshold, indicating the SCNRs above which the MSE of the ML estimate is reasonably close to the CRB, can be lowered by increasing . The noncoherent MIMO radar ambiguity function (AF) is developed in two different ways and illustrated by examples. It is shown for some specific examples that the size of the product controls the levels of the sidelobes of the AF.
2009 IEEE/SP 15th Workshop on Statistical Signal Processing, 2009
Previously, the Generalized Likelihood Ratio Test -Linear Quadratic (GLRT-LQ) has been extended to the Multiple-Input Multiple-Output (MIMO) case where all transmitreceive subarrays are considered jointly as a system such that only one detection threshold is used. The new MIMO detector is Constant False Alarm Rate (CFAR) with respect to the clutter power fluctuations. In this paper, the adaptive version of this detector is considered, as well as a fluctuating target model similar to that of the Swerling Target. The degradation of the detection performance due to the estimation of the covariance matrix and the fluctuation of the target is studied through simulations for both the well-known Optimum Gaussian Detector (OGD) and the new MIMO detector under Gaussian and non-Gaussian clutter.
Non-coherent MIMO radar for target estimation: More antennas means better performance
2009 43rd Annual Conference on Information Sciences and Systems, 2009
This paper presents an analysis of the joint estimation of target location and velocity using multiple-input multiple-output (MIMO) radar. A theorem is formulated on the asymptotic properties of the maximum likelihood (ML) estimate The joint Cramer-Rao bound (CRB) is calculated for a Rayleigh fluctuating extended target. The mean square error (MSE) of the ML estimate is analyzed for orthogonal Gaussian pulses. It is shown that the signal to noise ratio (SNR) boundary between low and high MSE values can be lowered by increasing the number of antennas. The non-coherent MIMO radar ambiguity function (AF) is developed and illustrated by examples. It is shown that the product of the number of transmit and receive antennas can control the sidelobes level of the AF.
Moving Target Parameters Estimation in Noncoherent MIMO Radar Systems
IEEE Transactions on Signal Processing, 2012
The problem of estimating the parameters of a moving target in multiple-input multiple-output (MIMO) radar is considered and a new approach for estimating the moving target parameters by making use of the phase information associated with each transmit-receive path is introduced. It is required for this technique that different receive antennas have the same time reference, but no synchronization of initial phases of the receive antennas is needed and, therefore, the estimation process is noncoherent. We model the target motion within a certain processing interval as a polynomial of general order. The rst three coefcients of such a polynomial correspond to the initial location, velocity, and acceleration of the target, respectively. A new maximum likelihood (ML) technique for estimating the target motion coefcients is developed. It is shown that the considered ML problem can be interpreted as the classic "overdetermined" nonlinear least-squares problem. The proposed ML estimator requires multidimensional search over the unknown polynomial coefcients. The Cramér-Rao bound (CRB) for the proposed parameter estimation problem is derived. The performance of the proposed estimator is validated by simulation results and is shown to achieve the CRB.
MIMO radar detection of targets in compound-gaussian clutter
Conference Record - Asilomar Conference on Signals, Systems and Computers, 2008
Multiple-input multiple-output (MIMO) radars with widely-separated transmitters and receivers are useful to discriminate a target from clutter using the spatial diversity of the scatterers in the illuminated scene. We consider the detection of targets in compound-Gaussian clutter fitting for example scatterers with heavy-tailed distributions for high-resolution and/or low-grazing-angle radars in the presence of sea or foliage clutter. First, we introduce a data model using the inverse gamma distribution to represent the clutter texture. Then, we apply the parameter-expanded expectation-maximization (PX-EM) algorithm to estimate the clutter texture and speckle, as well as the target parameters. We develop a generalized likelihood ratio (GLR) test target detector using the estimates and show the advantages of MIMO using Monte Carlo simulations.