Parameter Optimization of Generalized S Transform Based on Improved Genetic Algorithm

摘要

Background: For the traditional Fourier Transform (FT), it cannot effectively detect thefrequency of non-stationary signals with time. Analyzing the local characteristics of time-varyingsignal by using FT is hard to achieve. Therefore, many time-frequency analysis methods which canmeet different needs have been proposed on the basis of the traditional Fourier transform, like theShort Time Fourier Transform (STFT), the widely used Continuous Wavelet Transform (CWT),Wigner-Ville Distribution (WVD) and so on. However, the best time and frequency resolution cannotbe achieved at the same time due to the uncertainty criterion.Methods: From the point of view of optimizing time-frequency performance, a new Generalized STransform (GST) window function optimization method is proposed by combining time frequencyaggregation with an improved genetic algorithm in this paper.Results: In the noiseless condition, the Linear Frequency Modulation (LFM), Nonlinear FrequencyModulation (NLFM) and binary Frequency Shift Keying (2FSK) signals are simulated. The simulationresults prove that the method can improve the time-frequency concentration and decreasing Rényientropy effectively. Compared with other four traditional time-frequency analysis methods, theimproved GST has more advantages.Conclusion: In this paper, a new method of optimizing the window function in GST based on improvedGA is proposed in this paper. Experiments on LFM, NLFM and 2FSK signals show that theproposed method not only has the advantages of high resolution, but also determines the optimal parametersvia the time frequency focusing criterion and the Rényi entropy. Compared with the otherfour kinds of time-frequency analysis methods, the optimized GST based on improved GA in thispaper has the best time-frequency focusing.