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| Paper: | SPTM-P1.5 |
| Session: | System Identification and Parameter Estimation |
| Time: | Tuesday, May 18, 13:00 - 15:00 |
| Presentation: |
Poster |
| Topic: |
Signal Processing Theory and Methods: System Modeling, Representation, & Identification |
| Title: |
FAST AND APPROXIMATIVE ESTIMATION OF CONTINUOUS-TIME STOCHASTIC SIGNALS FROM DISCRETE-TIME DATA |
| Authors: |
Magnus Mossberg; Karlstad University | | |
| | Erik K Larsson; Uppsala University | | |
| Abstract: |
A fast and approximative method for estimating continuous-timestochastic disturbance signals, described as continuous-timeautoregressive moving average processes, from discrete-time data is presented. First, it is shown how these processes can be regarded as continuous-time autoregressive processes and therelation between the two types of processes is derived. The relation is then used for mapping estimated autoregressive parameters from an instrumental variable approach ontoautoregressive moving average parameters. The procedure provides a solution to the estimation problem that preserves the continuous-time parameterization. |
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