Name
Title | ||
|---|---|---|
An iterative Kalman smoother/least-squares algorithm for the identification of delta-ARX models |
Abstract | ||
|---|---|---|
Additive measurement noise on the output signal is a significant problem in the δ-domain and disrupts parameter estimation of auto-regressive exogenous (ARX) models. This article deals with the identification of δ-domain linear time-invariant models of ARX structure (i.e. driven by known input signals and additive process noise) by using an iterative identification scheme, where the output is also corrupted by additive measurement noise. The identification proceeds by mapping the ARX model into a canonical state-space framework, where the states are the measurement noise-free values of the underlying variables. A consequence of this mapping is that the original parameter estimation task becomes one of both a state and parameter estimation problem. The algorithm steps between state estimation using a Kalman smoother and parameter estimation using least squares. This approach is advantageous as it avoids directly differencing the noise-corrupted 'raw' signals for use in the estimation phase and uses different techniques to the common parametric low-pass filters in the literature. Results of the algorithm applied to a simulation test problem as well as a real-world problem are given, and show that the algorithm converges quite rapidly and with accurate results. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1080/00207720903428872 | Int. J. Systems Science |
Keywords | Field | DocType |
delta-arx model,parameter estimation problem,parameter estimation,estimation phase,least-squares algorithm,additive measurement noise,significant problem,state estimation,real-world problem,original parameter estimation task,simulation test problem,disrupts parameter estimation,delta operator,least square,auto regressive,linear time invariant,smoothing,low pass filter,state space,state space model | Least squares,Iterative method,Control theory,Kalman filter,Parametric statistics,Smoothing,Estimation theory,System identification,Mathematics,Parameter identification problem | Journal |
Volume | Issue | ISSN |
41 | 7 | 0020-7721 |
Citations | PageRank | References |
1 | 0.37 | 17 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. A. Chadwick | 1 | 5 | 0.83 |
| S. R. Anderson | 2 | 23 | 2.27 |
| V. Kadirkamanathan | 3 | 355 | 39.25 |