Non Stationary Signal Analysis, Energy Demodulation and the Multicomponent AM--FM Signal Model

Technical reportThis report addresses the problem of non-stationary signal analysis and multicomponent AM–FM signal separation and demodulation, which are important and significant problems in the area of non stationary signal modeling. This problem appears in various guises in the areas of communications, where it appears in the problem of the cochannel or adjacent channel problem, in biomedical signal processing, where it appears in the area of heart-beat sound modeling, radar signal processing, where it is used to model high frequency radar clutter, in speech processing where it appears in the form of AM–FM speech analysis and synthesis, and image processing, where it finds application in image demodulation and texture analysis. Although numerous multicomponent AM–FM signal analysis algorithms exist, these algorithms are unable to effect signal separation and demodulation in the general case and work only in narrow ranges of component spectral separation or relative power ratios. These algorithms specifically experience singularity problems when the components overlap spectrally, when one of the components is much stronger than the others or when the component instantaneous frequency tracks overlap. Numerous component enumeration methods have been studied for stationary multicomponent sinusoidal signals exist but such a framework does not exist for non-stationary signal component enumeration. The Teager–Kaiser energy operator, related energy separation algorithm and higher-order generalizations of the operator have recently gained recognition for monocomponent demodulation due to their simplicity, efficiency, and excellent time resolution. The goal of this proposal is to develop energy based multicomponent AM–FM demodulation algorithms, that incorporate component enumeration, with the above mentioned difficult scenarios in mind. The developed algorithms will then be applied to (a) cochannel and adjacent channel interference problem, (b) ocean clutter supression and multiple target tracking problem, (c) second heart beat sound separation and the maternal-foetal ECG signal separation problem