1, Get code method
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2, Introduction to synchronous compression transformation
Based on WT, SST uses synchronous compression operator to improve the resolution of time-frequency ridge in time spectrum, and realizes the extraction and reconstruction of instantaneous frequency. set up ψ (b) Is the wavelet generating function, then the continuous wavelet transform of signal x(t) is:
x(t) - vibration signal;
W(a,b) - continuous wavelet transform result of x(t);
t -- time variable;
a - scale factor;
b -- translation factor;
According to the analysis, the instantaneous frequency information of position (a,b) in the wavelet domain is:
ω x(a,b) - instantaneous frequency;
j -- imaginary unit.
Literature  found that no matter what value a is, the oscillation characteristics of W(a,b) on B point to the initial frequency Ω. Therefore:
According to the defined synchronous compression transform, the inverse wavelet transform is:
x(b) - result of inverse wavelet transform;
C ψ—— Phase difference coefficient;
ψ (a ξ)—— Wavelet generating function.
yes ω x(a,b) is integrated along the direction of scale a and classified into frequency domain ω=ω At the position of x(a,b), the synchronous compression transformation is defined as:
Sst ( ω, b) - synchronous compression function of signal B;
ω—— Angular frequency.
The result of equation (6) and phase difference coefficient C ψ, The amplitude of the signal is reduced to the position in the frequency domain, and finally the high-resolution time spectrum is obtained.
3, Partial source code
% A numerical signal. clear; SampFreq = 100; t1 = 0 : 1/SampFreq : 6; t2 = 6+1/SampFreq : 1/SampFreq : 14-1/SampFreq; t = [t1 t2]; Sig1 = [sin(2*pi*((25+8)*t1 + 10*sin(t1))) sin(2*pi*(34.2+8)*t2) ]; Sig2 = sin(2*pi*(8*t+3*atan((t - 5).^2))); Sig=Sig1+Sig2; [m,n]=size(Sig); time=(1:n)/SampFreq; fre=(SampFreq/2)/(n/2):(SampFreq/2)/(n/2):(SampFreq/2); Ts = SST(Sig',50); figure imagesc(time,fre,abs(Ts)); axis xy ylabel('Freq / Hz'); xlabel('Time / Sec') title('SST'); df=fre(2)-fre(1); %Signal Reconstrucion %reconstruted region of Sig1 is 20-45Hz. s1=real(sum(Ts(20/df:45/df,:))); figure plot(s1);hold on; plot(Sig1,'r-'); title('Reconstructed signal (blue),original signal (red)'); %reconstruted region of Sig1 is 1-15Hz. s2=real(sum(Ts(1/df:15/df,:))); figure plot(s2);hold on; plot(Sig2,'r-'); title('Reconstructed signal (blue),original signal (red)'); %It provides a perfect reconstrution performance. %Maybe you cannot see the bule signal clearly. function [Ts] = SST(x,hlength); % Computes the SST (Ts) of the signal x. % INPUT % x : Signal needed to be column vector. % hlength: The hlength of window function. % OUTPUT % Ts : The SST [xrow,xcol] = size(x); if (xcol~=1), error('X must be column vector'); end; if (nargin < 1), error('At least 1 parameter is required'); end; if (nargin < 2), hlength=round(xrow/5); end; Siglength=xrow; hlength=hlength+1-rem(hlength,2); ht = linspace(-0.5,0.5,hlength);ht=ht'; % Gaussian window h = exp(-pi/0.32^2*ht.^2); % derivative of window dh = -2*pi/0.32^2*ht .* h; % g' [hrow,hcol]=size(h); Lh=(hrow-1)/2; N=xrow; t=1:xrow; [trow,tcol] = size(t); tfr1= zeros (N,tcol) ; tfr2= zeros (N,tcol) ; tfr= zeros (round(N/2),tcol) ; Ts= zeros (round(N/2),tcol) ; for icol=1:tcol, ti= t(icol); tau=-min([round(N/2)-1,Lh,ti-1]):min([round(N/2)-1,Lh,xrow-ti]); indices= rem(N+tau,N)+1; rSig = x(ti+tau,1); %rSig = hilbert(real(rSig)); tfr1(indices,icol)=rSig.*conj(h(Lh+1+tau)); tfr2(indices,icol)=rSig.*conj(dh(Lh+1+tau)); end; tfr1=fft(tfr1); tfr2=fft(tfr2); tfr1=tfr1(1:round(N/2),:); tfr2=tfr2(1:round(N/2),:); ft = 1:round(N/2); bt = 1:N;
4, Operation results
5, matlab version and references
1 matlab version
 Shen Zaiyang. Proficient in MATLAB signal processing [M]. Tsinghua University Press, 2015
 Gao Baojian, Peng Jinye, Wang Lin, pan Jianshou. Signal and system -- Analysis and implementation using MATLAB [M]. Tsinghua University Press, 2020
 Wang Wenguang, Wei Shaoming, Ren Xin. MATLAB implementation of signal processing and system analysis [M]. Electronic Industry Press, 2018
 Gao Yanyan, Zhang Jing, Li Li, Jia Yingxi. Design of teaching demonstration system of digital signal processing based on GUI [J]. Education and Teaching Forum. 2019, (48)
 Li Jun, Zhang Shuling, Shuai Jing. Digital signal processing aided teaching system based on Matlab GUI interface [J]. Information communication. 2020, (08)
 Wang Bing, Wei Zhiheng, Wang Wenbin, Dai Yuanting, Zhao Minamata Jun. application of order analysis method based on synchronous compression transformation in bearing fault diagnosis of urban rail transit vehicles [J]. Research on urban rail transit. 2021,24 (07)