Home HomeLloyd J. Griffiths, University of Colorado (U.S.A.)
This paper addresses the issue of employing space-time adaptive processing (STAP) prior to Doppler filtering in radar systems. When STAP beamformer processing is applied to spatial/temporal samples that include successive radar pulses, the adaptive weights can cause modulation (spreading) of the desired target Doppler. In this paper, a linearly constrained adaptive beamformer is proposed that ensures pre-Doppler adaptive processing will not degrade desired signal coherence. A formulation of the processor is presented and its properties described. Specific procedures for estimating the required covariance matrix using relatively few data samples are also provided. Examples showing the application of the proposed structure to recorded data are used to illustrate its performance. Comparisons are made with adaptive systems that do not employ constraints to illustrate the advantages of the proposed system. An extension to the full STAP system which employ time taps in both range and pulse number is describe
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Lloyd J. Griffiths, University of Colorado (U.S.A.)
Shiann-Shiun Jeng, The University of Texas at Austin (U.S.A.)
Hsin-Piao Lin, The University of Texas at Austin (U.S.A.)
Garret Okamoto, The University of Texas at Austin (U.S.A.)
Guanghan Xu, The University of Texas at Austin (U.S.A.)
Wolfhard Vogel, The University of Texas at Austin (U.S.A.)
Antenna arrays can be employed in mobile communications to increase channel capacity as well as communication quality via spatially selective reception/transmission at base stations. In most wireless communications systems, directions of arrival of multipath signals need to be found for spatial selective transmission. Unfortunately, due to the coherent nature of multipath signals, it is quite difficult to find their directions of arrival. In this paper, we will present a subspace smoothing algorithm for finding the directions of arrival of multipath signals based on the mobile terminal signals received at different time instances. More importantly, we will present our experimental results to demonstrate that the spatial diversity is present for slight movements of a mobile terminal and that the subspace smoothing approach is effective in real wireless scenarios. All of the experiments were performed using the smart antenna testbed at the University of Texas at Austin.
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Kainam T. Wong, Purdue University (U.S.A.)
Michael D. Zoltowski, Purdue University (U.S.A.)
A closed-form multi-dimensional multi-invariance generalization of the ESPRIT algorithm is introduced to exploit the entire invariance structure underlying a (possibly) multi-parametric data model, thereby greatly improving estimation performance. The multiple-invariance data structure that this proposed method can handle includes: (1) multiple occurrence of one size of invariance along one or multiple parametric dimensions, (2) multiple sizes of invariances along one or multiple parametric dimensions, and (3) invariances that cross over two or more parametric dimensions. The basic (uni-dimensional uni-invariance) ESPRIT algorithm is applied in parallel to each multiple pair of matrix-pencils characterizing the multiple invariance relationships in the data model, producing multiple sets of cyclically ambiguous estimates over the multi-dimensional parameter space. A weighted least-squares hyper-plane is then fitted to these set of estimates to yield very accurate and unambiguous estimates of the signal parameters.
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Timothy A. Barton, MIT Lincoln Laboratory (U.S.A.)
Steven T. Smith, MIT Lincoln Laboratory (U.S.A.)
Adaptive algorithms require a good estimate of the interference covariance matrix. In situations with limited sample support such an estimate is not available unless there is structure to be exploited. In applications such as space-time adaptive processing (STAP) the underlying covariance matrix is structured (e.g., block Toeplitz), and it is possible to exploit this structure to arrive at improved covariance estimates. Several structured covariance estimators have been proposed for this purpose. The efficacy of several of these are analyzed in this paper in the context of a variety of STAP algorithms. The SINR losses resulting from the different methods are compared. An example illustrating the superior performance resulting from a new maximum likelihood algorithm (based upon the expectation-maximization algorithm) is demonstrated using simulation and experimental data.
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Javier Sanchez-Araujo, LSS-SUPELEC (France)
Sylvie Marcos, LSS-SUPELEC (France)
Several works reported in the literature show that the subspace-based linear methods are computationally much more interesting than the eigendecomposition-based techniques and only slightly less accurate from the statistical point of view. They therefore have a clear potential for real time applications. We here retain the basic ideas behind this class of methods and we formulate the subspace tracking problem as a classical adaptive least squares (LS) one. Solving this adaptive LS problem results in subspace tracking algorithms of computational complexity linearly proportional to the sample vector dimension. We suggest a possible implementation for tracking the direction-of-arrival (DOA) of slowly moving sources using the LS approach. The problem of estimating crossing targets is also discussed and we propose an efficient strategy to deal with it.
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Frédéric Dublanchet, LSS-CNRS (France)
Jérôme Idier, LSS-CNRS (France)
Patrick Duvaut, ETIS (France)
We address the problem of identification of sinusoidal components from observed data, which is fundamental for array signal processing and spectral line decomposition. Joint detection and estimation are proposed in a unified Bayesian framework, so that no preliminary estimate of the number of signals is required. All unknown quantities are estimated from a unique regularized ``stochastic'' likelihood function, including the number of sources and statistical parameters. The impulsive solution is modeled as a continuous Poisson-Gaussian process. A powerful iterative technique is proposed to maximize the posterior likelihood. Simulation results show that the method behaves particularly well for small data sets, even for a single experiment.
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Yifeng Zhou, Telexis (Canada)
Henry Leung, DREO (Canada)
Patrick Yip, CRL, McMaster University (Canada)
Qu Jin, Mitel (Canada)
In this paper, we present an entropy based approach for DOA estimation in Gaussian and non-Gaussian environments. The DOA estimates are obtained by minimizing an entropy measure of the array data in the noise subspace. We show that the entropy approach leads to the MAP algorithm under the Gaussian assumption. Under the non-Gaussian assumption, we apply the varimax norm as an information measure. An intuitive consistency analysis is also performed. Computer simulations are used to demonstrate the effectiveness of the proposed approach.
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Jian Li, University of Florida (U.S.A.)
Petre Stoica, University of Uppsala (Sweden)
Zheng-She Liu, University of Florida (U.S.A.)
We present a comparative study of using the IQML (iterative quadratic maximum likelihood) algorithm and the MODE (method of direction estimation) algorithm for direction-of-arrival estimation with a uniform linear array. The consistent condition and the theoretical mean-squared error for the parameter estimates of IQML are presented. The computational complexities of both algorithms are also compared. We show that the frequency estimates obtained via MODE are asymptotically statistically efficient, while those obtained via IQML are almost always inconsistent and hence inefficient. We also show that the amount of computations required by IQML is usually much larger than that required by MODE, especially for low signal-to-noise ratio and large number of snapshots.
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Kristine Bell, George Mason University (U.S.A.)
Yariv Ephraim, George Mason University (U.S.A.)
Harry Van Trees, George Mason University (U.S.A.)
An adaptive beamformer which is robust to uncertainty in source DOA is derived. The beamformer is a weighted sum of minimum variance distortionless response (MVDR) beamformers pointed at a set of candidate DOAs, where the relative contribution of each MVDR beamformer is determined from a combination of observed data and prior knowledge about the DOA. When SNR is high, the MVDR beamformer whose look direction is closest to the source dominates, and nearly optimal performance is obtained. When SNR is low, the weighted combination of beamformers has a wider main beam which is robust to DOA uncertainty.
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Panagiotis Tsakalides, University of Southern California (U.S.A.)
Nikias Chrysostomos, University of Southern California (U.S.A.)
This paper studies methods for Space-Time Adaptive Processing-based (STAP-based) parameter estimation in the presence of impulsive noise backgrounds. Towards this goal, the theory of alpha-stable random processes provides an elegant and mathematically tractable framework for the solution of the detection and parameter estimation problems in the presence of impulsive radar clutter. Our goal is to develop joint target angle and Doppler, maximum likelihood-based estimation techniques from radar measurements retrieved in the presence of severe clutter modeled as an alpha-stable, complex random process. We derive the Cramer-Rao bounds for the additive Cauchy interference scenario to assess the best-case estimation accuracy which can be achieved. The results are of great importance in the study of space-time adaptive processing (STAP) for airborne pulse Doppler radar arrays operating in impulsive interference environments.
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Jacob Sheinvald, RAFAEL (Israel)
Mati Wax, RAFAEL (Israel)
Anthony J. Weiss, Tel-aviv University (Israel)
We consider the problem of localizing multiple narrow-band stationary signals using an arbitrary time-varying array such as an array mounted on a moving platform. We assume a Gaussian stochastic model for the received signals and employ the Generalized Least Squares (GLS) estimator to get an asymptotically-efficient estimation of the model parameters. In case the signals are a-priori known to be uncorrelated, this estimator allows to exploit this prior knowledge to its benefit. For the important case of translational motion of a rigid array, a computationally-efficient spatial-smoothing method is presented. Simulation results confirming the theoretical results are included.
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Amir Leshem, Hebrew University Jerusalem (Israel)
Mati Wax, RAFAEL (Israel)
We present an algorithm for the calibration of sensor arrays in the presence of multipath. The algorithm is based on two sets of calibration data obtained from two angularly separated transmitting points. Simulation results demonstrating the performance of the algorithm are included, as well as presentation of the Maximum Likelihood estimator for the problem.
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