The robust generalized least-squares estimator
| Autor: | Mohamed L. Hambaba |
|---|---|
| Rok vydání: | 1992 |
| Předmět: |
Stationary process
Mean squared error Noise spectral density Estimator Generalized least squares Noise Control and Systems Engineering Frequency domain Signal Processing Statistics Computer Vision and Pattern Recognition Electrical and Electronic Engineering Algorithm Software Impulse response Mathematics |
| Zdroj: | Signal Processing. 26:359-368 |
| ISSN: | 0165-1684 |
| DOI: | 10.1016/0165-1684(92)90120-l |
| Popis: | This paper addresses the problem of robust linear estimations of systems perturbed by noise with a wide sense stationary process (WSS). The noise spectral density is known only to be in a neighborhood of some specified spectral density. Additionally the system's impulse response function is assumed to be a random process. A generalized least-squares estimator (GLS) in the frequency domain is considered and it is demonstrated that where the Fourier transform is applied to the observed data, robust estimation occurs. The experiment shows that the sample maximum variance depends on noise contamination for large data segments and depends on the upper bound of the robust method for short data segments. The proposed approach is simple to implement and can be very effective in several practical applications. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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