Signal spectrum analysis and period estimation by using delayed signal sampling
Lorenzo Peretto, G. Pasini, Carlo Muscas
Abstract
Lorenzo Peretto, G. Pasini, Carlo Muscas
Abstract
The paper describes a signal power spectrum analyzer and a period signal estimator whose bandwidth is not limited by the mean sampling time. The procedure relays on the evaluation of the input signal autocorrelation function in different delayed time instants, located at either equi-spaced or random time instants. To this, a recursive random sampling process in the time domain was used in order to avoid any bandwidth limitation due to the sampling strategy in the evaluation of each autocorrelation function. The signal power spectrum as well as its period, provided that an approximate value of the fundamental frequency is known, can finally be evaluated. Some theoretical background and experimental work is reported in the paper for validating the performance of the method.
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The paper describes a signal power spectrum analyzer and a period signal estimator whose bandwidth is not limited by the mean sampling time. The procedure relays on the evaluation of the input signal autocorrelation function in different delayed time instants, located at either equi-spaced or random time instants. To this, a recursive random sampling process in the time domain was used in order to avoid any bandwidth limitation due to the sampling strategy in the evaluation of each autocorrelation function. The signal power spectrum as well as its period, provided that an approximate value of the fundamental frequency is known, can finally be evaluated. Some theoretical background and experimental work is reported in the paper for validating the performance of the method.
Key concepts: Autocorrelation, Bandwidth (computing), Estimator, SIGNAL (programming language), Sampling (signal processing), Spectral density, Computer science, Coherent sampling