Abstract:
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.
