Signal processing based on parameter structural modeling and separation of highly correlated signals of known structure

Abstract

Results in the study of signal processing based on the use of parameter structural modeling (PSM) are presented. First, we introduce a special form of time-series modeling based on signal-dependent building blocks. Such modeling is used in the design of a nested-form transversal structure, known as a composite filter, based on a shift-invariant finite impulse resonse (FIR) as well as infinite impulse response (IIR) building blocks. The newly proposed composite PSM model (CPSM) possesses a unique feature, namely, its ability to suppress one signal of a given structure, while at the same time being ideally transparent to another one. The intrinsic property of this proposed CPSM is its enhanced insensitivity with respect to noise as well as its ability to fast track, in contrast to the commonly used linear line-enhancer based on conventional autoregressive moving average (ARMA), thus leading to a more practically sound processing of short-duration signals. It is shown that the proposed time-series modeling based on CPSM can be effectively applied towards the separation of superimposed signals of heavily overlapping spectra. Next, the parameter-invariant nonlinear structural signal representation based on shift-invariant CPSM is presented. The use of this model in the design of annihilation operators (AO) is described, and composite parameter-free structural modeling (CPFSM) is developed. Based on this model, two canonical forms of the parameter-invariant null filters (PINF) are presented, and their use in the suppression of a given class of signals, independently of the values of their a priori unknown parameters, is illustrated. The paper also presents some simulation examples illustrating the application of the proposed CPSM and CPFSM in solving problems of detection and parameter estimation in the presence of highly non-Gaussian, mainly signal-like interferences.