| 1. |
- Ottersten, Björn, 1961-, et al.
(författare)
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A Subspace Based Instrumental Variable Method for State-Space System Identification
- 1994
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Ingår i: Proceedings of 10th IFAC Symposium on System Identification. ; , s. 139-144
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Konferensbidrag (refereegranskat)abstract
- Traditional prediction-error techniques for multivariable system identification require canonical descriptions using a large number of parameters. This problem may be avoided using subspace based methods, since these estimate a state-space model directly from the data. In this paper, a subspace based technique for identifying general finite-dimensional linear systems is presented and analyzed. Similar to subspace based identification schemes, the space spanned by the extended observability matrix is first estimated. The system parameters are then extracted by reparametrizing the nullspace of the subspace estimate in terms of the coefficients of the characteristic polynomial. A quadratic problem is obtain and based on a statistical analysis, an optimal weighting derived.
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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. |
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| 5. |
- Ottersten, Björn, 1961-, et al.
(författare)
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Parametric Direction Estimation from Antenna Array Data Based on Calibration Information
- 1994
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Ingår i: Proc. of Nordic Antenna Symposium.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- In antenna array signal processing we are often concerned with accurate determination of signal parameters from array measurements. To obtain accurate direction estimates, knowledge of the array response is required. Most estimation techniques are sensitive to errors in the array response model. Thus, reliable array calibration schemes are of great importance. In practice, calibration is done by measuring the array response when only one emitter is present and the signal parameters of which are allowed to vary in a known way. Herein, methods for generating an array model from noise corrupted calibration vectors is developed. The performance of the technique is examined on data collected from a full-scale antenna array in the 870 MHz region.
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| 6. |
- Ottersten, Björn, 1961-, et al.
(författare)
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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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| 7. |
- 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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| 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. |
- Stoica, Petre, et al.
(författare)
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Optimal Localization of Partially Known Signals in Unknown Noise Fields
- 1994
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Ingår i: Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing ICASSP-94. - : IEEE. ; , s. 217-220
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Konferensbidrag (refereegranskat)abstract
- Most methods for sensor array signal processing require the covariance matrix of the background noise to beknown. Various techniques for overcoming this limitation have recently been proposed. While most of these are based on assumptions on the noise, we present herein an alternative approach based on partial knowledge of thesignals. Methods yielding minimum variance estimates for the model in question are presented and analyzed.
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| 10. |
- Viberg, Mats, et al.
(författare)
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A Comparison of Model-Based Detection and Adaptive Sidelobe Cancelling for Radar Array Processing
- 1994
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Ingår i: Proc. of Nordic Antenna Symposium.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- The recent development of fast A/D converters and digital signal processors has considerably affected the modern radar system design. In the most popular configuration to date, the main channel (a conventional beamformer) is digitized along with a number of auxiliary channels. This configuration forms the basis for the adaptive sidelobe canceller (ASLC), which has been proposed for mitigating the influence of jammers that are present in the sidelobes of the main channel of the array. The ASLC can be efficiently implemented in real-time using recursive least-squares techniques, and has been demonstrated to perform well in certain scenarios. However, the ASLC has a number of shortcomings. The method fails, for instance when the target signal is too strong. This drawback can be eliminated by applying a parametric approach. Herein, the exact maximum likelihood (ML) estimator assuming a sinusoidal target signal is derived. The computational complexity of the ML estimator is found to be comparable to that of the ASLC. Initial simulation results indicate that the ML and ASLC methods perform similar at low SNR's, but that the ML estimator does not share the signal cancellation phenomena observed in thw ASLC.
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