Weight extracting transform for instantaneous frequency estimation and signal reconstruction

This paper introduces a novel time-frequency analysis method called the Weight Extracting Transform (WET). The primary objective of WET is to enhance the energy concentration in linear time-frequency representations for time-varying signals. By aggregating the time- frequency blur produced by the short -time Fourier transform onto the actual instantaneous frequency, WET improves the readability and accuracy of the time-frequency representation. Additionally, the algorithm is extended to a more general linear time-frequency transform known as the chirplet transform, which effectively handles fast-varying signals. The WET method excels at distinguishing components with closely spaced instantaneous frequencies and can reconstruct the original signal from its time-frequency representation. Experimental results using simulated and real -world signals demonstrate that WET achieves superior energy concentration and noise robustness compared to existing methods.