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15.
Isaksson, Alf
(författare)
Identification of Time Varying Systems and Application of System Identification to Signal Processing
1986
Licentiatavhandling (övrigt vetenskapligt/konstnärligt) abstract
Part IA new approach to identification of time varying systems is presented, and evaluated using computer simulations. The new approach is built upon the similarities between recursive least squares identification and Kalman filtering.The parameter variations are modelled as process noise in a state space model and then identified using adaptive Kalman filtering. A method for adaptive Kalman filtering is derived and analysed. The simulations indicate that this new approach is superior to previous methods based on adjusting the forgetting factor. This improvement is however gained at the price of a signification increase in computational complexity.Part IIIn this part we apply parameter estimation to the problem of transmission line protection.One approach based on recursive least squares identification is presented. The method has ben tested using simulated data generated by the program EMTP.Another approach based on the theory of travelling waves is also discussed.Part IIIIn this part a method for input estimation or deconvolution is presented. The basis of the method is to use a parametrized model the input signal. To use the method we should thus be able to express the input signal as a function of some unknown parameters and time. The algorithms simultaneously estimates the parameters of the input signal and the parameters of the system transfer function. The presentation here is restricted to transfer functions of all pole type, i.e. ARX-models. The method can be extended to handle zeros in the transfer function. The computational burden would however increase significantly. The algorithm uses efficient numerical methods, as for instance QR-factorization thorugh Householder transformation.The algorithm is in this paper applied to a problem in speech coding. It has been observed that the quality of synthesized speech can be improved, if a more detailed model than an impulse train is used for the pitch pulses, see Fant (1980). It is here shown how the method presented in this paper can be used to estimate the system parameters of the speech production and the parameters of the glottal pulse simultaneously.
16.
Isaksson, Alf
(författare)
On System Identification in one and two Dimensions with Signal Processing Applications
1988
Doktorsavhandling (övrigt vetenskapligt/konstnärligt) abstract
This thesis consists of four parts, with system identification as the common theme. The first part studies the asymptotic properties of two-dimensional identification methods. In the second part an approach to identification of time varying systems is presented. Part three applies system identification to the problem of transmission line protection. Finally part four deals with input estimation in speech coding.Part I is devoted to system identification in two dimensions. First we study the asymptotic properties of the estimates as the number of data tends to infinity. The main objective is to investigate what happens if the model order also tends to infinity. The focus is on frequency expressions of the extimation variance. The analysis covers both the least squares method for causal models, and the maximum likelihood method for noncausal models.In Part II we study one approach to identification of time varying sytems. The parameter variations are modelled as process noise in a state space model, and identified using adaptive Kalman filtering. A method for adaptive Kalman filtering is derived and analysed. The simulations indicate that this new approach is superior to previous methods based on adjusting the forgetting factor. The improvement is however gained at the price of a significant increase in computational complexity.Part III describes the use of recursive identification in protective relaying. The Fourier coefficients of voltage and current are estimated using recursive least squares identification. The estimates are then used to detect short circuits. The method is evaluated using data generated by the standard program EMTP.In Part IV a method for inverse glottal filtering is presented. The basis of the method is to use a parameterized model of the input signal, i.e. the glottal pulses. The algorithm simultaneously estimates the parameters of the input signal and the parameters of the system transfer function, the vocal tract model. The presentation is restricted to transfer functions of all-pole type.
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18.
Ottersten, Björn, et al.
(författare)
Analysis of Subspace Fitting and ML Techniques for Parameter Estimation from Sensor Array Data
1989
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.
19.
Ottersten, Björn, et al.
(författare)
Performance Analysis of the Total Least Squares ESPRIT Algorithm
1989
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.
20.
Roy, R, et al.
(författare)
Recent Advances in Multidimensional Sensor Array Signal Processing
1989
Ingår i: Proceedings of the 6th ASSP Multidimensional Signal Processing Workshop.
Konferensbidrag (refereegranskat) abstract
Summary form only given, ESPRIT is a patented technique for high-resolution estimation of signal parameters that 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, with virtually no loss in performance as measured by parameter estimate variance. Whereas ESPRIT only requires that the sensor array possess a single invariance best visualized by considering tow identical but otherwise arbitrary arrays of sensors displaced (but not rotated) with respect to each other, many arrays currently in use in various applications are uniform arrays of identical sensor elements with displacements in more than one dimension. The uniformly sampled phased-array radar is a typical example, and such systems are commonplace in high-resolution direction finding systems. Such arrays possess many invariances in potentially more than one dimension. Recent developments in extending the concepts behind ESPRIT to multiple invariances and multidimensional parameter spaces were examined.
