Resolution enhancement in time-frequency distributions based on adaptive time extrapolations

Many time-frequency distribution (TFD) implementations involve block processing of samples of the signal under analysis. The extraction of a finite amount of data and the windowing that often follows it, reduce frequency domain resolution. This paper illustrates the possible benefits that can be achieved by applying a resolution enhancement (superresolution) step to each block of samples before it is used in the computation of a TFD. The data extension is performed here with adaptive weighted norm extrapolation, a technique that produces a stationary extension of the given block of data to a prescribed length. Examples are shown where the approach is used in block processing implementations of the spectrogram, the Choi-Williams, and the adaptive radially Gaussian distributions