Name
Abstract | ||
|---|---|---|
Nonlinear system identification has been studied under the assumption that the noise has finite second and higher order statistics. In many practical applications, impulsive measurement noise severely weakens the effectiveness of conventional methods. In this paper, α-stable noise is used as a noise model. In such case, the minimum mean square error (MMSE) criterion is no longer an appropriate metric for estimation error due to the lack of finite second-order statistics of the noise. Therefore, we adopt minimum dispersion criterion, which in turn leads to the adaptive least mean pth power (LMP) algorithm. It is shown that the LMP algorithm under the α-stable noise model converges as long as the step size satisfies certain conditions. The effect of p on the performance is also investigated. Compared with conventional methods, the proposed method is more robust to impulsive noise and has better performance. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/TSP.2005.849213 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
adaptive filters,adaptive signal processing,higher order statistics,identification,impulse noise,least mean squares methods,nonlinear systems,adaptive Volterra filter,adaptive least mean pth power algorithm,exstable noise,higher order statistics,impulsive environment,impulsive noise,minimum dispersion criterion,minimum mean square error criterion,nonlinear system identification,Adaptive Volterra filter,LMP algorithm,impulsive noise,nonlinear system identification | Dispersion (optics),Mathematical optimization,Nonlinear system,Control theory,Higher-order statistics,Nonlinear system identification,Minimum mean square error,Adaptive filter,Impulse noise,Gaussian noise,Mathematics | Journal |
Volume | Issue | ISSN |
53 | 7 | 1053-587X |
Citations | PageRank | References |
35 | 1.11 | 7 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Binwei Weng | 1 | 176 | 13.82 |
| Kenneth E Barner | 2 | 354 | 39.58 |