원문정보
초록
영어
S-transform is a new time-frequency analysis method, which is deduced from short-time Fourier transform and continue Wavelet transform. It has much better performance than traditional time-frequency method. Therefore, in this paper, the basic principle of is briefly introduced and the relationships between is analyzed by theoretical derivation. According to the simulation experiments, the time-frequency space characteristics of short-time Fourier transform, Wigner-Ville distribution and S-transform are contrasted. As the results shown, the window of S-transform has a progressive frequency dependent resolution. So the S-transform has a great flexibility and utility in the processing of non-stationary signal. Compare with the time-frequency spectrum of three different analysis methods under various noise conditions, it is obvious that S-transform has much better anti-noise performance than that of traditional methods for non-stationary signal processing. Based on the superior time-frequency resolution, the S-transform spectrum can be used to describe the structure of incoming signal effectively.
목차
1. Introduction
2. The Introduction of S-Transform
2.1. Deduce S-transform from Short-time Fourier Transform
2.2. Deduce S-transform from Continue Wavelet Transform
2.3. The inverse S-transform
3. The Discrete S-transform
3.1. Deduce S-transform from Short-time Fourier Transform
3.2. Deduce S-transform from Continue Wavelet Transform
4. Comparison Task
4.1. The Time-frequency Comparison Task
4.2. The Anti-noise Property Comparison Task
4.3. The Ability to Distinguish Different Signals
5. Conclusion
Acknowledgements
References