| 1. |
- Gunnarsson, Svante, et al.
(author)
-
Some asymptotic results in recursive identification using Laguerre models
- 1990
-
In: Proceedings of the IEEE Conference on Decision and Control. - Honolulu, HI, USA : Linköping University. ; , s. 1068-1073
-
Conference paper (peer-reviewed)abstract
- Frequency domain expressions for the quality of recursively identified Laguerre models are presented. These models generalize finite impulse response (FIR) models by using a priori information about the dominating time constants of the system to be identified. Expressions for the model quality are derived under the assumptions that the system varies slowly, that the model is updated slowly, and that the model order is high. The model quality is evaluated by investigating the properties of the estimated transfer function, and explicit expressions for the mean square error (MSE) of the transfer function, and explicit expressions for the mean square error of the transfer function estimate are derived.
|
|
| 2. |
- Andersson, Sören, et al.
(author)
-
A Study of Adaptive Arrays for Mobile Communication Systems
- 1990
-
Reports (other academic/artistic)abstract
- The application of adaptive antenna techniques to increase the channel capacity in mobile radio communication is discussed. Directional sensitivity is obtained by using an antenna array at the base station, possibly both in receiving and transmitting mode. A scheme for separating several signals at the same frequency is proposed. The method is based on high-resolution direction finding following by optimal combination of the antenna outputs. Comparisons to a method based on reference signals are made. Computer simulations are carried out to test the applicability of the technique to scattering scenarios that typically arise in urban areas. The proposed scheme is found to have great potential in rejecting cochannel interference, albeit at the expense of high computational requirements.
|
|
| 3. |
- Krishnamurthy, Vikram, et al.
(author)
-
Factorizations that relax the positive real condition in continuous-time and fast-sampled ELS schemes
- 1990
-
In: International journal of adaptive control and signal processing (Print). - : Wiley. - 0890-6327 .- 1099-1115. ; 4:5, s. 389-414
-
Journal article (peer-reviewed)abstract
- This paper proposes extended least-squares (ELS) for ARMAX model identification of continuous-time and certain discrete-time systems. The schemes have a relaxed strictly positive real (SPR) condition for global convergence. The relaxed SPR scheme is achieved by introducing overparametrization and prefiltering but without introducing ill-conditioning. The schemes presented are the first such proposed for continuous-time systems. The concepts developed in continuous time carry through to fast-sampled continuous-time systems and associated discrete-time ELS algorithms. For such situations, in comparison with previously proposed discrete-time schemes, the degree of overparametrization required in the proposed scheme of this paper is significantly lower. The reduction is achieved by using more suitable prefiltering and overparametrization techniques than previously proposed. We also establish the persistence of excitation (PE) of the regression vectors in the proposed ELS schemes to assure strong consistency, obtain convergence rates and provide robustness to unmodelled dynamics. To prove the PE of continuous-time regression vectors, we develop output reachability characterization for MIMO linear continuous-time systems.
|
|
| 4. |
- Ljung, Lennart, 1946-, et al.
(author)
-
Influence of Model Order on Change Detection in Noise-Free Complex Systems
- 1990
-
In: Proceedings of the 1990 American Control Conference. - Linköping : Linköping University. ; , s. 2388-2393
-
Reports (other academic/artistic)abstract
- In all adaptation problems it is essential to estimate the system's (or signal's) characteristics as quickly as possible. There are several design variables that effect this ability. Forgetting factors in recursive algorithms, band-pass filtering to select interesting frequency ranges, and similar, are of major importance for this problem. Also the model order will affect the speed of adaptation since it influences both the "noise" level arising from unmodelled dynamics and the "variance" that depends on the number of estimated parameters. We discuss in this contribution the role of the model order for adaptation. The conclusion is that provided appropriate regularization is applied there are no disadvantages in the use of (very) high order models other than the computational burden.
|
|
| 5. |
- Wahlberg, Bo, 1959-
(author)
-
The effects of rapid sampling in system identification
- 1990
-
In: Automatica. - : Elsevier. - 0005-1098 .- 1873-2836. ; 26:1, s. 167-170
-
Journal article (peer-reviewed)abstract
- This paper deals with the effects of rapid sampling in system identification. In continuous time, noise models of non-zero relative degree imply that one has to differentiate the measured data to find the prediction error parameter estimate. This problem can be avoided by using noise models of relative degree zero or by introducing prefilters. The corresponding difficulty for the discrete time case is less obvious. However, by linking the discrete time and continuous time parameter estimation problem, we show that the same problem arises for rapid sampled systems.
|
|
| 6. |
- Ljung, Lennart, et al.
(author)
-
Influence of model order on change detection in noise-free complex system
- 1990
-
In: Proceedings of the American Control Conference. - San Diego, CA, USA. ; , s. 2388-2393
-
Conference paper (peer-reviewed)abstract
- A study is made of the accuracy that could be obtained in the transfer function estimate. The system is noise-free but complex. It is found that it is better to use a (very) high-order model in combination with suitable regularization (prior). The reason is that the potential problem with an ill-conditioned regression matrix (high variance) is counteracted by the regularization. The bandpass filters cause information delay in the transient phase and degrade model-error attenuation in the stationary phase. It is also shown how set membership identification can handle model errors and provide hard (and in the example provided, somewhat conservative) error bounds. For this, the a priori information and the fact that the system is noise-free are used. An additive, bounded disturbance would not cause problems. The actual set membership estimate (the center of the model set) does not differ very much from the least-squares estimate in the example (because of the particular choice of prior).
|
|
| 7. |
- Wahlberg, Bo, et al.
(author)
-
Approximate Modelling by Means of Orthonormal Functions
- 1990
-
Reports (other academic/artistic)abstract
- ARX, AR and FIR modeling are generalized by repacing the delay operator by discrete Laguerre/Kautz filters. The aim is to obtain useful low order approximate models of complex systems by using a priori information about the dominating time constants of the system. An important characteristic of these models are that they can be written in a linear regressions form. Hence, the least squares methods can be applied for system identification. By deriving naturally associated state-space realizations of Laguerre/Kautz models, we obtain model representations that are more directly suitable for control design etc. The orthonormal property of Laguerre/Kautz functions is very important, since it guarantees a Toeplitz structure of the corresponding least squares normal equations. This property enables us to determine persistence of excitation conditions and analyze asymp totic statistical properties of Laguerre/Kautz model estimates.
|
|
| 8. |
- Wahlberg, Bo, 1959-
(author)
-
ARMA spectral estimation of narrow-band processes via model reduction
- 1990
-
In: IEEE Transactions on Acoustics, Speech, and Signal Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 0096-3518. ; 38:7, s. 1144-1154
-
Journal article (peer-reviewed)abstract
- The problem of estimating autoregressive moving average (ARMA) models for narrowband processes is considered. The following approach is proposed. Estimate a high-order autoregressive (AR) approximation of the process. By model reduction, based on a truncated internally balanced realization or optimal Hankel-norm model reduction, reduce the order of this high-order AR estimate to find a lower-order ARMA model. This algorithm gives ARMA spectral estimates with excellent resolution properties, without using iterative numerical minimization methods as for the maximum-likelihood method. How to take the narrowband assumption into account in the model reduction step is discussed in detail.
|
|
| 9. |
- Wahlberg, Bo
(author)
-
Factorizations that Relax the Positive Real Condition in Continuous-time ELS Schemes
- 1990
-
In: Proceedings of the 11th IFAC World Congress. ; , s. 209-214
-
Conference paper (peer-reviewed)abstract
- This paper proposes Extended Least Squares (ELS) schemes for ARMAX model identification of continuous-time systems. The schemes have a relaxed Strictly Positive Real (SPR) condition for global convergence. The relaxed SPR scheme is achieved by introducing overparametrisation and prefiltering but without introducing ill-conditioning. The schemes presented are the first such proposed for continuous-time systems. The concepts developed here carry through to output-error, fast-sampled continuous-time systems and associated discrete-time ELS algorithms. We also state conditions for the persistence of excitation (P.E.) of the regression vectors in the proposed ELS schemes to assure strong consistency and obtain convergence rates.
|
|
| 10. |
- Wahlberg, Bo, et al.
(author)
-
Hard Frequency-Domain Model Error Bounds from Least-Squares Like Identification Techniques
- 1990
-
Reports (other academic/artistic)abstract
- The problem of deriving so-called hard-error bounds for estimated transfer functions is addressed. A hard bound is one that is sure to be satisfied, i.e. the true system Nyquist plot will be confined with certainty to a given region, provided that the underlying assumptions are satisfied. By blending a priori knowledge and information obtained from measured data, it is shown how the uncertainty of transfer function estimates can be quantified. The emphasis is on errors due to model mismatch. The effects of unmodeled dynamics can be considered as bounded disturbances. Hence, techniques from set membership identification can be applied to this problem. The approach taken corresponds to weighted least-squares estimation, and provides hard frequency-domain transfer function error bounds. The main assumptions used in the current contribution are: that the measurement errors are bounded, that the true system is indeed linear with a certain degree of stability, and that there is some knowledge about the shape of the true frequency response.
|
|
