| 1. |
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| 2. |
- Ottersten, Björn, et al.
(författare)
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Analysis of Subspace Fitting and ML Techniques for Parameter Estimation from Sensor Array Data
- 1989
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Rapport (övrigt vetenskapligt/konstnärligt)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, et al.
(författare)
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Asymptotic Results for Sensor Array Processing
- 1989
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Ingår i: Proceedings of the 1989 International Conference on Acoustics, Speech and Signal Processing. - Linköping : Linköping University. ; , s. 2266-2269
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The emphasis of this study is on direction-of-arrival (DOA) estimation of narrowband emitter signals. There are two main signal models that have been used in the sensor array problem, namely, stochastic and deterministic. A discussion is presented of the models, their effect on the Cramer-Rao lower bounds (CRB), cases where the maximum likelihood (ML) estimator asymptotically achieves this bound, and how the bounds are related. It is shown that the asymptotic variance of the ML method based on the stochastic model is lower than that of the ML method based on the deterministic model. The stochastic ML variance and CRB are based on the assumption of Gaussian emitter signals, which can be an unrealistic assumption when narrowband signals are assumed. The authors show that the ML estimator based on the Gaussian signal model achieves the stochastic CRB asymptotically, regardless of the signal distribution. Numerical evaluations of the theoretical variances for different scenarios are presented to clarify the relationship between the methods and bounds discussed.
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| 4. |
- Ottersten, Björn, et al.
(författare)
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Exact and Large Sample ML Techniques for Parameter Estimation and Detection in Array Processing
- 1991
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Rapport (övrigt vetenskapligt/konstnärligt)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.
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| 5. |
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| 6. |
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| 7. |
- Ottersten, Björn, et al.
(författare)
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Performance Analysis of the Total Least Squares ESPRIT Algorithm
- 1989
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Rapport (övrigt vetenskapligt/konstnärligt)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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| 8. |
- Stoica, Petre, et al.
(författare)
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Instrumental Variable Approach to Array Processing in Spatially Correlated Noise Fields
- 1991
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Rapport (övrigt vetenskapligt/konstnärligt)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 proposed in 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 noise covariance 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. |
- Viberg, Mats, et al.
(författare)
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Array Processing in Correlated Noise Fields Using Instrumental Variables and Subspace Fitting
- 1993
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- An improved technique for direction-of-arrival estimation of temporally correlated signals in the presence of spatially colored, but temporally uncorrelated, noise is presented. The method is particularly suited to applications in which the receiver bandwidth exceeds that of the emitter signals. A statistical performance analysis shows that the method nearly achieves the deterministic Cramer-Rao bound if the signals are sufficiently predictable. A Monte Carlo experiment suggests that the theoretical estimation error variance well predicts the empirical mean square error down to the threshold region.
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| 10. |
- Viberg, Mats, et al.
(författare)
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Detection and Estimation in Sensor Arrays using Weighted Subspace Fitting
- 1989
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The problem of signal parameter estimation of narrowband emitter signals impinging on an array of sensors is addressed. A multidimensional estimation procedure that applies to arbitrary array structures and signal correlation is proposed. The method is based on the recently introduced weighted subspace fitting (WSF) criterion and includes schemes for both detecting the number of sources and estimating the signal parameters. A Gauss-Newton-type method is presented for solving the multidimensional WSF and maximum-likelihood optimization problems. The global and local properties of the search procedure are investigated through computer simulations. Most methods require knowledge of the number of coherent/noncoherent signals present. A scheme for consistently estimating this is proposed based on an asymptotic analysis of the WSF cost function. The performance of the detection scheme is also investigated through simulations.
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