This research is concerned with the Power Spectrum Density Estimation with em- phasIze on the bigh-resolution algorithms and their real-time implementations. Tl-ie classical PSD estimation methods are fast and robust. but their resolutions may not be adequate when the record length is short. On the other hand when the record length is short the autoregressive parametric methods have higher resolution capability, but they may have spurious peaks if the order of the model is chosen too high in the attempt to increase the resolution when the SNR is low. An algorithm is proposed to combine the spectrum of the classical method and the autoregressive model. This allows the overestimation of the order of the autoregressive model. The spuriot-is peaks that result are then suppressed by the low values in the spectrum of the classical nict liods. I'lic wide specl ral mairilobe of the classical method, on the other liand, serves to indicate the area where the true signals are located. This alleviates the difficult order selection problem of the parametric methods. An adaptive version of this method is also proposed. It is based on the adaptive autoregressive and adaptive maximum eigenvector concept. It can track a slowly changing environment. With I lie combination of these txN, o methods. it is shown that it. has the high-resolution performance of AR method ýN, ith improved performance in the noisy environment.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:359499 |
| Date | January 1993 |
| Creators | Sung-Yuan, Ko |
| Contributors | Russell, D. D. |
| Publisher | Cranfield University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://dspace.lib.cranfield.ac.uk/handle/1826/4197 |
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