An adaptive digital filtering scheme is developed to deal with the problem of improving the signal-to-noise ratio of a signal corrupted by noise when only general {\em a priori} assumptions regarding the signal and the noise are possible. Specifically, the noise is assumed to be white zero-mean and uncorrelated; while the signal is considered to be band-limited, possibly with slowly varying spectrum. The proposed adaptive digital filtering scheme is based upon a class of variable wave digital filters. Adaptation of the digital filter multipliers is accomplished through the use of an identification procedure based on an adaptive spectral estimation method.

This paper presents an algorithmic method for measuring the instantaneous frequency of a uniformly sampled FM signal. The measured parameter, termed digital instantaneous frequency, is defined in a manner similar to that used to describe frequency-modulated, continuous-time signals. The measurements are derived from an adaptive linear prediction spectral estimates. The proposed algorithm is utilized in the development of a digital processor for FM demodulation which operates on a uniformly sampled FM signal, and its output is a sampled sequence of the estimated demodulated message. The performance of the digital processor is demonstrated and compared with that of a conventional FM discriminator.