An Improved Generalized S-Transform Time-Frequency Analysis Method Based on Parameter Optimization

Abstract

The invention discloses a parameter optimization based time frequency analysis method for an improved generalized S-transform. The parameter optimization based time-frequency analysis method comprises the steps of firstly selecting the range of parameters in the improved generalized S-transform; for each group of parameters, calculating time frequency distribution of an input signal by using the improved generalized S-transform and carrying out energy normalization processing on the time frequency distribution; then calculating the corresponding time frequency aggregation degree of each group of parameters and employing one group of parameters corresponding to maximum values as optimized parameters; finally introducing the optimized parameters into the improved generalized S-transform to calculate the time frequency distribution of the input signal. According to the parameter optimization based time frequency analysis method for the improved generalized S-transform, a first-order function with frequency as an independent variable is introduced into a window function of the S-transform so that the form of the window function is flexible and adjustable, the parameters of the first-order function are optimized through the time frequency aggregation degree, the energy aggregation of the signal time frequency distribution can be improved, the estimated accuracy of signal instantaneous parameters is enhanced, the calculated amount is small, the parameter optimization based time frequency analysis method is suitable for analysis and processing of communication, radar, earthquake and biomedicine signals.

Description

technical field [0001] The invention belongs to the field of digital signal processing, and in particular relates to an analysis method of non-stationary signals. The time-frequency analysis method of improved generalized S transform based on parameter optimization can be used for analysis and processing of communication, radar, earthquake and biomedical signals. Background technique [0002] Various types of non-stationary signals widely exist in practical engineering, and the analysis and processing of non-stationary signals is a research hotspot in the field of digital signal processing. Unlike stationary signals, the frequency of non-stationary signals changes with time, so its analysis requires a two-dimensional time-frequency analysis method combining time domain and frequency domain. [0003] Common time-frequency analysis methods include short-time Fourier transform (STFT), Wigner-Ville distribution (WVD), wavelet transform (WT) and S-transform (ST). The window func...

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Problems solved by technology

For example, in 1997, Manshiha et al. proposed to replace the parameter f in the window function with f / r, which can properly adjust the form of the window function. The calculation is simple, but the adjustment ability is limited; Pinnegar et al. proposed in 2003 that the standard deviation of the window function The hyperbolic window function is tuned and the window is asymmetrical. The time window in the high frequency band is narrow, and the window function with better symmetry is selected to improve the frequency resolution. The time window in the low frequency band is wider, and the asymmetric window is selected function, but the calculation of the hyperbolic equation involving two parameters is more complicated; Chen Xuehua proposed in 2005 to introduce two parameters λ and p into the window function, using λ|f| p Replacing the parameter f further increases the flexibility of the window function adjustment, but it involves the exponential operation of the frequency f, and the amount of calculation is also large

[0005] The improved window functions in the above-mentioned generalized S-transform have their own characteristics. The more flexible the parameter adjustment, the greater the calculation amount, and the selection of parameters is limited by the principle of uncertainty, that is, the time resolution and frequency resolution cannot be optimal at the same time. , need to compromise

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