| 1. |
- Viberg, Mats, 1961, et al.
(författare)
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Maximum Likelihood Array Processing in Spatially Correlated Noise Fields Using Parameterized Signals
- 1997
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Ingår i: IEEE Transactions on Signal Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1941-0476 .- 1053-587X. ; 45, s. 996-1004
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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 a 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.
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| 2. |
- Ottersten, Björn, 1961-, et al.
(författare)
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Analysis of Subspace Fitting and ML Techniques for Parameter Estimation from Sensor Array Data
- 1992
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Ingår i: IEEE Transactions on Signal Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1053-587X .- 1941-0476. ; 40:3, s. 590-600
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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.
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| 3. |
- Ottersten, Björn, 1961-, et al.
(författare)
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Asymptotic Robustness of Sensor Arrary Processing Methods
- 1990
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Ingår i: Proceedings of the 1990 International Conference on Acoustics, Speech and Signal Processing. - Linköping : IEEE Signal Processing Society. ; , s. 2635-2638
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Konferensbidrag (refereegranskat)abstract
- Methods for estimating the parameters of narrowband signals arriving at an array of sensors are analyzed. Asymptotic results for several estimators have recently appeared in the literature. With few exceptions, the previous analysis requires the incident signal waveforms to be Gaussian random variables. These results are shown to be valid under much more general conditions, i.e. the actual distribution of the signal waveforms does not affect the asymptotic properties of the parameter.
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| 4. |
- Ottersten, Björn, 1961-, et al.
(författare)
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Direction-of-Arrival Estimation for Wideband Signals using the ESPRIT Algorithm
- 1990
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Ingår i: IEEE Transactions on Acoustics, Speech and Signal Processing. - : IEEE Signal Processing Society. - 0096-3518. ; 38:2, s. 317-327
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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.
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| 5. |
- Ottersten, Björn, 1961-, et al.
(författare)
-
Performance Analysis of the Total Least Squares ESPRIT Algorithm
- 1991
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Ingår i: IEEE Transactions on Signal Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1053-587X .- 1941-0476. ; 39:5, s. 1122-1135
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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.
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| 6. |
- Ottersten, Björn, 1961-, et al.
(författare)
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Robust Source Localization Based on Local Array Response Modeling
- 1992
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Ingår i: Proceedings of the 1992 IEEE International Conference on Acoustics, Speech and Signal Processing. - Linköping : IEEE. - 0780305329 ; , s. 441-444 vol.2
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Konferensbidrag (refereegranskat)abstract
- Many practical applications of signal processing require accurate determination of signal parameters from sensor array measurements. Most estimation techniques are sensitive to errors in the array response model. Thus, reliable array calibration schemes are of great importance. A paradigm for generating an array model from noise corrupted calibration vectors is developed. The key idea is to use a local parametric model of the sensor responses. The potential improvement using the suggested scheme is demonstrated on real data collected from a full-scale hydroacoustic array.
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| 7. |
- Ottersten, Björn, et al.
(författare)
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Stochastic maximum likelihood estimation in sensor arrays by weighted subspace fitting
- 1989
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Ingår i: Conference Record - Asilomar Conference on Circuits, Systems & Computers. - Pacific Grove, CA, USA : Linköping University. ; , s. 599-603, s. 599-603
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Konferensbidrag (refereegranskat)abstract
- The problem of estimating parameters of multiple narrowband emitter signals from sensor array data is considered. Under the assumption of Gaussian distributed emitter signals, the stochastic maximum-likelihood (ML) technique is known to provide statistically efficient estimates, i.e., it achieves the Cramer-Rao bound (CRB). A multidimensional signal subspace method, termed weighted subspace fitting (WSF), has recently been proposed. It is shown that the WSF and ML estimates are asymptotically identical (for large data records). As a consequence, the WSF method is asymptotically efficient, assuming temporally white Gaussian signal waveforms and noise.
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| 8. |
- Stoica, Petre, et al.
(författare)
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Instrumental Variable Approach to Array Processing in Spatially Correlated Noise Fields
- 1994
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Ingår i: IEEE Transactions on Signal Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1053-587X .- 1941-0476. ; 42:1, s. 121-133
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Tidskriftsartikel (refereegranskat)abstract
- High-performance signal parameter estimation from sensor array data is a problem which has received much attention. A number of so-called eigenvector (EV) techniques such as MUSIC, ESPRIT, WSF, and MODE have been proposedin the literature. The EV techniques for array processing require knowledge of the spatial noise correlation matrix that constitutes a significant drawback. A novel instrumental variable (IV) approach to the sensor array problem is proposed. The IV technique relies on the same basic geometric properties as the EV methods to obtain parameter estimates. However, by exploiting the temporal correlation of the source signals, no knowledge of the spatial noisecovariance is required. The asymptotic properties of the IV estimator are examined and an optimal IV method is derived. Computer simulations are presented to study the properties of the IV estimators in samples of practical length. The proposed algorithm is also shown to perform better than MUSIC on a full-scale passive sonar experiment
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| 9. |
- Swindlehurst, A., et al.
(författare)
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A subspace fitting method for identification of linear state-space models
- 1995
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Ingår i: IEEE Transactions on Automatic Control. - : IEEE Control Systems Society. - 0018-9286 .- 1558-2523. ; 40:2, s. 311-316
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Tidskriftsartikel (refereegranskat)abstract
- A new method is presented for the identification of systems parameterized bylinear state-space models. The method relies on the concept of subspacefitting, wherein an input/output data model parameterized by the statematrices is found that best fits, in the least-squares sense, the dominantsubspace of the measured data. Some empirical results are included to illustrate the performance advantage of the algorithm compared to standard techniques
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| 10. |
- Swindlehurst, A. Lee, et al.
(författare)
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Multiple Invariance ESPRIT
- 1992
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Ingår i: IEEE Transactions on Signal Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1053-587X .- 1941-0476. ; 40:4, s. 867-881
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Tidskriftsartikel (refereegranskat)abstract
- A subspace-fitting formulation of the ESPRIT problem is presented that provides a framework for extending the algorithm to exploit arrays with multiple invariances. In particular, a multiple invariance (MI) ESPRIT algorithm is developed and the asymptotic distribution of the estimates is obtained. Simulations are conducted to verify the analysis and to compare the performance of MI ESPRIT with that of several other approaches. The excellent quality of the MI ESPRIT estimates is explained by recent results which state that, under certain conditions, subspace-fitting methods of this type are asymptotically efficient.
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