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  • Viberg, M, et al. (författare)
  • Maximum Likelihood Array Processing in Spatially Correlated Noise Fields Using Parametrized Signals
  • 1997
  • Ingår i: IEEE Transactions on Signal Processing. - 1053-587X. ; 45:4, s. 996-1004
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper deals with the problem of estimating signal parameters using an array of sensors. This problem is of interest in a variety of applications, such as radar and sonar source localization. A vast number of estimation techniques have been proposed in the literature during the past two decades. Most of these can deliver consistent estimates only if the covariance matrix of the background noise is known. In many applications, the aforementioned assumption is unrealistic. Recently, a number of contributions have addressed the problem of signal parameter estimation in unknown noise environments based on various assumptions on the noise. Herein, a different approach is taken. We assume instead that the signals are partially known. The received signals are modeled as linear combinations of certain known basis functions. The exact maximum likelihood (ML) estimator for the problem at hand is derived, as well as computationally more attractive approximation. The Cramer-Rao lower bound (CRB) on the estimation error variance is also derived and found to coincide with the CRB, assuming an arbitrary deterministic model and known noise covariance.
  • Stoica, Petre, et al. (författare)
  • Maximum Likelihood Array Processing for Stochastic Coherent Sources
  • 1996
  • Ingår i: In IEEE Trans. on Signal Processing. - IEEE Signal Processing Society. - 1053-587X. ; 44:1, s. 96-105
  • Tidskriftsartikel (refereegranskat)abstract
    • Maximum likelihood (ML) estimation in array signal processing for the stochastic noncoherent signal case is well documented in the literature. We focus on the equally relevant case of stochastic coherent signals. Explicit large-sample realizations are derived for the ML estimates of the noise power and the (singular) signal covariance matrix. The asymptotic properties of the estimates are examined, and some numerical examples are provided. In addition, we show the surprising fact that the ML estimates of the signal parameters obtained by ignoring the information that the sources are coherent coincide in large samples with the ML estimates obtained by exploiting the coherent source information. Thus, the ML signal parameter estimator derived for the noncoherent case (or its large-sample realizations) asymptotically achieves the lowest possible estimation error variance (corresponding to the coherent Cramer-Rao bound).
  • Viberg, Mats, et al. (författare)
  • Analysis of state space system identification methods based on instrumental variables and subspace fitting
  • 1997
  • Ingår i: Automatica. - 0005-1098. ; 33:9, s. 1603-1616
  • Tidskriftsartikel (refereegranskat)abstract
    • Subspace-based state-space system identification (4SID) methods have recently been proposed as an alternative to more traditional techniques for multivariable system identification. The advantages are that the user has simple and few design variables, and that the methods have robust numerical properties and relatively low computational complexities. Though subspace techniques have been demonstrated to perform well in a number of cases, the performance of these methods is neither fully understood nor analyzed. Our principal objective is to undertake a statistical investigation of subspace-based system identification techniques. The studied methods consist of two steps. The subspace spanned by the extended observability matrix is first estimated. The asymptotic properties of this subspace estimate are derived herein. In the second step, the structure of the extended observability matrix is used to find a system model estimate. Two possible methods are considered. The simplest one only uses a certain shift-invariance property, while in the other method a parametric representation of the null-space of the observability matrix is exploited. Explicit expressions for the asymptotic estimation error variances of the corresponding pole estimates are given.
  • Kaiser, Thomas, et al. (författare)
  • When will smart antennas be ready for the market? : Part II - results
  • 2005
  • Ingår i: IEEE signal processing magazine (Print). - 1053-5888. ; 22:6, s. 174-176
  • Tidskriftsartikel (övrigt vetenskapligt)abstract
    • The aim of this two-part forum is to shed more light on the future of smartantennas (SA) through discussions among a balanced group of experts from academia and industry. In part I, which appeared in the March 2005 issue of IEEE Signal Processing Magazine, each of the experts stated his own opinion after exchanging some thoughts by e-mail. Then, a panel session took place at ICAS-SP'05 and a public poll followed. Now, in part II, the results are summarized by the experts. The central topic of the forum was the expectedmarket breakthrough of SA.
  • Mowlér, Marc, et al. (författare)
  • Joint estimation of mutual coupling, element factor, and phase center in antenna arrays
  • 2007
  • Ingår i: EURASIP Journal on Wireless Communications and Networking. - 1687-1472. ; 2007:1, s. 030684
  • Tidskriftsartikel (refereegranskat)abstract
    • A novel method is proposed for estimation of the mutual coupling matrix of an antenna array. The method extends previous work by incorporating an unknown phase center and the element factor (antenna radiation pattern) in the model, and treating them as nuisance parameters during the estimation of coupling. To facilitate this, a parametrization of the element factor based on a truncated Fourier series is proposed. The performance of the proposed estimator is illustrated and compared to other methods using data from simulations and measurements, respectively. The Cramer-Rao bound (CRB) for the estimation problem is derived and used to analyze how the required amount of measurement data increases when introducing additional degrees of freedom in the element factor model. We find that the penalty in SNR is 2.5 dB when introducing a model with two degrees of freedom relative to having zero degrees of freedom. Finally, the tradeoff between the number of degrees of freedom and the accuracy of the estimate is studied. A linear array is treated in more detail and the analysis provides a specific design tradeoff.
  • Ottersten, Björn, 1961-, et al. (författare)
  • Analysis of Subspace Fitting and ML Techniques for Parameter Estimation from Sensor Array Data
  • 1992
  • Ingår i: IEEE Transactions on Signal Processing. - 1053-587X. ; 40:3, s. 590-600
  • Tidskriftsartikel (refereegranskat)abstract
    • It is shown that the multidimensional signal subspace method, termed weighted subspace fitting (WSF), is asymptotically efficient. This results in a novel, compact matrix expression for the Cramer-Rao bound (CRB) on the estimation error variance. The asymptotic analysis of the maximum likelihood (ML) and WSF methods is extended to deterministic emitter signals. The asymptotic properties of the estimates for this case are shown to be identical to the Gaussian emitter signal case, i.e. independent of the actual signal waveforms. Conclusions concerning the modeling aspect of the sensor array problem are drawn.
  • Ottersten, Björn, 1961-, et al. (författare)
  • Direction-of-Arrival Estimation for Wideband Signals using the ESPRIT Algorithm
  • 1990
  • Ingår i: IEEE Transactions on Acoustics, Speech and Signal Processing. - IEEE Signal Processing Society. - 0096-3518. ; 38:2, s. 317-327
  • Tidskriftsartikel (refereegranskat)abstract
    • A novel direction-of-arrival estimation algorithm is proposed that applies to wideband emitter signals. A sensor array with a translation invariance structure is assumed, and an extension of the ESPRIT algorithm for narrowband emitter signals is obtained. The emitter signals are modeled as the stationary output of a finite-dimensional linear system driven by white noise. The array response to a unit impulse from a given direction is represented as the impulse response of a linear system. The measured data from the sensor array can then be seen as the output of a multidimensional linear system driven by white noise sources and corrupted by additive noise. The emitter signals and the array output are characterized by the modes of the linear system. The ESPRIT algorithm is applied at the poles of the system, the power of the signals sharing the pole is captured, and the effect of noise is reduced. The algorithm requires no knowledge, storage, or search of the array manifold, as opposed to wideband extensions of the MUSIC algorithm. This results in a computationally efficient algorithm that is insensitive to array perturbations. Simulations are presented comparing the wideband and ESPRIT algorithm to the modal signal subspace method and the coherent signal subspace method.
  • Ottersten, Björn, 1961-, et al. (författare)
  • Exact and Large Sample ML Techniques for Parameter Estimation and Detection in Array Processing
  • 1993
  • Ingår i: Radar Array Processing. - Berlin ; New York : Springer Berlin/Heidelberg. - 3-540-55224-3 (Berlin : acid-free paper) - 978-3-540-55224-6 (Berlin : acid-free paper) - 0-387-55224-3 (New York : acid-free paper) - 978-0-387-55224-8 (New York : acid-free paper) ; s. 99-151
  • Bokkapitel (övrigt vetenskapligt)abstract
    • Sensor array signal processing deals with the problem of extracting information from a collection of measurements obtained from sensors distributed in space. The number of signals present is assumed to be finite, and each signal is parameterized by a finite number of parameters. Based on measurements of the array output, the objective is to estimate the signals and their parameters. This research area has attracted considerable interest for several years. A vast number of algorithms has appeared in the literature for estimating unknown signal parameters from the measured output of a sensor array.
  • Ottersten, Björn, 1961-, et al. (författare)
  • Performance Analysis of the Total Least Squares ESPRIT Algorithm
  • 1991
  • Ingår i: IEEE Transactions on Signal Processing. - 1053-587X. ; 39:5, s. 1122-1135
  • Tidskriftsartikel (refereegranskat)abstract
    • The asymptotic distribution of the estimation error for the total least squares (TLS) version of ESPRIT is derived. The application to a uniform linear array is treated in some detail, and a generalization of ESPRIT to include row weighting is discussed. The Cramer-Rao bound (CRB) for the ESPRIT problem formulation is derived and found to coincide with the asymptotic variance of the TLS ESPRIT estimates through numerical examples. A comparison of this method to least squares ESPRIT, MUSIC, and Root-MUSIC as well as to the CRB for a calibrated array is also presented. TLS ESPRIT is found to be competitive with the other methods, and the performance is close to the calibrated CRB for many cases of practical interest. For highly correlated signals, however, the performance deviates significantly from the calibrated CRB. Simulations are included to illustrate the applicability of the theoretical results to a finite number of data.
  • ROY, R, et al. (författare)
  • ESPRIT and Uniform Linear Arrays
  • 1989
  • Ingår i: Proceedings of the 33rd SPIE International Technical Symposium : Advanced Algorithms and Architectures for Signal Processing IV. ; s. 370-380
  • Konferensbidrag (refereegranskat)abstract
    • ESPRIT is a recently developed and patented technique for high-resolution estimation of signal parameters. It exploits an invariance structure designed into the sensor array to achieve a reduction in computational requirements of many orders of magnitude over previous techniques such as MUSIC, Burg's MEM, and Capon's ML, and in addition achieves performance improvement as measured by parameter estimate error variance. It is also manifestly more robust with respect to sensor errors (e.g. gain, phase, and location errors) than other methods as well. Whereas ESPRIT only requires that the sensor array possess a single invariance best visualized by considering two identical but other-wise arbitrary arrays of sensors displaced (but not rotated) with respect to each other, many arrays currently in use in various applications are uniform linear arrays of identical sensor elements. Phased array radars are commonplace in high-resolution direction finding systems, and uniform tapped delay lines (i.e., constant rate A/D converters) are the rule rather than the exception in digital signal processing systems. Such arrays possess many invariances, and are amenable to other types of analysis, which is one of the main reasons such structures are so prevalent. Recent developments in high-resolution algorithms of the signal/noise subspace genre including total least squares (TLS) ESPRIT applied to uniform linear arrays are summarized. ESPRIT is also shown to be a generalization of the root-MUSIC algorithm (applicable only to the case of uniform linear arrays of omni-directional sensors and unimodular cisoids). Comparisons with various estimator bounds, including CramerRao bounds, are presented.
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