Novel results on frequency estimation and statistical characterization of three spectral estimation techniques

Abstract

The problem of detecting a sinusoidal signal in white noise and estimati on of its parameters is essentially a problem in signal processing such as radar, sonar, biomedical, etc. In this dissertation, various modern spectrum estimation approaches, Maximum Entropy Spectral Analysis (MESA) spectral moments and analytic signal techniques and their statistical characterization have been investigated and formulated in detail. The development of modern spectrum estimation known as parametric techniques for estimating parameters of sinusordal signals in white noise is important. Therefore the parameter estimation technique based on previously appeared and as well as some other newly developed modern spectrum estimation procedures have been presented in this dissertation. Comparative performances and drawbacks of most of the parametric techniques known as Maximum Likelihood (ML), Maximum Entropy (ME), Pisarenko, Kumerason, Prony methods used in frequency estimation have been summarized. The analytic signal model called as Argument method to estimate frequency and bandwidth of a sinusoidal signal is studied in detail. New expressions related to the expected value, variance and probability density function of estimate are derived analytically.