Abstract
The synchrosqueezing transform (SST) is an advanced post-processing method to sharpen the time–frequency representation (TFR). However, it still processes the signal in frequency domain. Therefore, it cannot effectively analyze signals whose energy is not well concentrated in frequency domain. The fractional S-transform (FrST) inherits the merits of the short-time fractional Fourier transform and the continuous wavelet transform, processing signals in fractional frequency domain. In this paper, a novel non-stationary signal processing tool, synchrosqueezing fractional S-transform (SSFrST) has been proposed, which combines the advantages of SST and FrST. First, we introduce a novel FrST and derive its fundamental properties. Second, based on the novel FrST, we propose SSFrST and discuss its reconstruction formula and implementation. It can not only improve time–frequency resolution, but also process signals in the time-fractional–frequency plane. Finally, we provide several applications to validate the effectiveness of our methods, including chirp signal parameters estimation, signal separation, filtering and noise separation.










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References
M. Bahri, R. Ashino, Some properties of windowed linear canonical transform and its logarithmic uncertainty principle. Int. J. Wavelets Multiresolut. Inf. Process. 14(03), 1650015 (2016)
V.C. Chen, H. Ling, Joint time-frequency analysis for radar signal and image processing. IEEE Signal Process. Mag. 16(2), 81–93 (1999)
X. Chen, J. Guan, Z. Bao, Y. He, Detection and extraction of target with micromotion in spiky sea clutter via short-time fractional fourier transform. IEEE Trans. Geosci. Remote Sens. 52(2), 1002–1018 (2014)
X. Chen, H. Chen, R. Li, Y. Hu, Y. Fang, Multisynchrosqueezing generalized S-transform and its application in tight sandstone gas reservoir identification. IEEE Geosci. Remote Sens. Lett. 19, 1–5 (2022)
C.K. Chui, Q. Jiang, L. Li, J. Lu, Time-scale-chirprate operator for recovery of non-stationary signal components with crossover instantaneous frequency curves. Appl. Comput. Harm. Anal. 54, 323–344 (2021)
V. Corretja, E. Grivel, Y. Berthoumieu, J.M. Quellec, T. Sfez, S. Kemkemian, Enhanced cohen class time-frequency methods based on a structure tensor analysis: applications to ISAR processing. Signal Process. 93(7), 1813–1830 (2013)
D. Cvetkovic, U.E. Derya, I. Cosic, Wavelet transform feature extraction from human PPG, ECG, and EEG signal responses to ELF PEMF exposures: a pilot study. Dig. signal Process. 18(5), 861–874 (2008)
I. Daubechies, The wavelet transform, time-frequency localization and signal analysis (Princeton University Press, Princeton, 2009)
I. Daubechies, M. Stéphane, A nonlinear squeezing of the continuous wavelet transform based on auditory nerve models. Wavelets Med. Biol. 5, 527–546 (2017)
I. Daubechies, J. Lu, H.T. Wu, Synchrosqueezed wavelet transforms: an empirical mode decomposition-like tool. Appl. Comput. Harmon. Anal. 30(2), 243–261 (2011)
X. De-Ping, G. Ke, Fractional S transform-Part 1: theory. Appl. Geophys. 9(1), 73–79 (2012)
J. Du, M.W. Wong, H. Zhu, Continuous and discrete inversion formulas for the Stockwell transform. Integral Transf. Special Funct. 18(8), 537–543 (2007)
L. Durak, O. Arikan, Short-time Fourier transform: two fundamental properties and an optimal implementation. IEEE Trans. Signal Process. 51(5), 1231–1242 (2003)
G.-R. Gillich, Z.-I. Praisach, Modal identification and damage detection in beam-like structures using the power spectrum and time-frequency analysis. Signal Process. 96, 29–44 (2014)
D.W. Griffin, J.S. Lim, Signal estimation from modified short-time Fourier transform. IEEE Trans. Acoust. Speech Signal Process. 32(2), 236–243 (1984)
Y. Guo, B.-Z. Li, L.-D. Yang, Novel fractional wavelet transform: principles, MRA and application. Dig. Signal Process. 110, 102937 (2021)
Y. He, Z. Jiang, M. Hu, Y.Z. Li, Local maximum Synchrosqueezing Chirplet transform: an effective tool for strongly nonstationary signals of gas turbine. IEEE Trans. Instrum. Meas. 70, 1–14 (2021)
Z. Huang, J. Zhang, T. Zhao, Y. Sun, Synchrosqueezing S-transform and its application in seismic spectral decomposition. IEEE Trans. Geosci. Remote Sens. 54(2), 817–825 (2015)
B.-Z. Li, Y.-P. Shi, Image watermarking in the linear canonical transform domain. Math. Probl. Eng. 2014(12), 1–9 (2014)
B.-Z. Li, R. Tao, Y. Wang, New sampling formulae related to linear canonical transform. Signal Process. 87(5), 983–990 (2007)
C.-P. Li, B.-Z. Li, T.-Z. Xu, Approximating bandlimited signals associated with the LCT domain from nonuniform samples at unknown locations. Signal Process. 92(7), 1658–1664 (2012)
C.-P. Li, B.-Z. Li, T.-Z. Xu, Adaptive short-time Fourier transform and synchrosqueezing transform for non-stationary signal separation. Signal Process. 166, 107231 (2020)
L. Li, H. Cai, Q. Jiang, Adaptive synchrosqueezing transform with a time-varying parameter for non-stationary signal separation. Appl. Comput. Harmon. Anal. 49(3), 1075–1106 (2020)
Y.-M. Li, D. Wei, L. Zhang, Double-encrypted watermarking algorithm based on cosine transform and fractional Fourier transform in invariant wavelet domain. Inf. Sci. 551, 205–227 (2021)
L. Li, N. Han, Q. Jiang, C.K. Chui, A chirplet transform-based mode retrieval method for multicomponent signals with crossover instantaneous frequencies. Dig. Signal Process. 120, 103262 (2022)
N. Liu, J. Gao, X. Jiang, Z. Zhang, Q. Wang, Seismic time-frequency analysis via STFT-based concentration of frequency and time. IEEE Geosci. Remote Sens. Lett. 14(1), 127–131 (2016)
B.A. Luis, The fractional Fourier transform and time-frequency representations. IEEE Trans. Signal Process. 42(11), 3084–3091 (1994)
B. Mawardi, T. Syamsuddin, L. Chrisandi, A generalized S-transform in linear canonical transform. J. Phys. Conf. Ser. 1341, 062005 (2019)
T. Oberlin, S. Meignen, V. Perrier, Second-order synchrosqueezing transform or invertible reassignment? towards ideal time-frequency representations. Science 2, 96 (2010)
De. N. Oliveira, R. José, J.B. Lima, Discrete fractional Fourier transforms based on closed-form Hermite-Gaussian-like DFT eigenvectors. IEEE Trans. Signal Process. 65(23), 6171–6184 (2017)
H.M. Ozaktas, B. Barshan, D. Mendlovic, L. Onural, Convolution, filtering, and multiplexing in fractional fourier domains and their relation to chirp and wavelet transforms. JOSA A 11(2), 547–559 (1994)
D.-H. Pham, S. Meignen, High-order synchrosqueezing transform for multicomponent signals analysis-with an application to gravitational-wave signal. IEEE Trans. Signal Process. 65(12), 3168–3178 (2017)
W. Qiu, B.-Z. Li, X.-W. Li, Speech recovery based on the linear canonical transform. Speech Commun. 55(1), 40–50 (2013)
G. Serbes, C.O. Sakar, Y.P. Kahya, N. Aydin, Pulmonary crackle detection using time-frequency and time-scale analysis. Dig. Signal Process. 23(3), 1012–1021 (2013)
J. Shi, X. Liu, N. Zhang, Generalized convolution theorem associated with fractional Fourier transform. Wirel. Commun. Mob. Comput. 14(13), 1340–1351 (2014)
J. Shi, J. Zheng, X. Liu, W. Xiang, Q. Zhang, Novel short-time fractional fourier transform: theory, implementation, and applications. IEEE Trans. Signal Process. 68, 3280–3295 (2020)
X. Shuiqing, H. Lei, C. Yi, H. Yigang, Nonuniform sampling theorems for bandlimited signals in the offset linear canonical transform. Circuits Syst. Signal Process. 37(8), 3227–3244 (2018)
S. Srdjan, About time-variant filtering of speech signals with time-frequency distributions for hands-free telephone systems. Signal Process. 80(9), 1777–1785 (2000)
H.M. Srivastava, F.A. Shah, A.Y. Tantary, A family of convolution-based generalized Stockwell transforms. J. Pseudo-Differ. Oper. Appl. 11(4), 1505–1536 (2020)
R.G. Stockwell, L. Mansinha, R.P. Lowe, Localization of the complex spectrum: the S transform. IEEE Trans. Signal Process. 44(4), 998–1001 (1996)
Y.-N. Sun, B.-Z. Li, Sliding discrete linear canonical transform. IEEE Trans. Signal Process. 66(17), 4553–4563 (2018)
R. Sun, Z. Yang, X. Chen, S. Tian, Y. Xie, Gear fault diagnosis based on the structured sparsity time-frequency analysis. Mech. Syst. Signal Process. 102, 346–363 (2018)
R. Tao, Y.-L. Li, Y. Wang, Short-time fractional Fourier transform and its applications. IEEE Trans. Signal Process. 58(5), 2568–2580 (2009)
Yu. Tao, S. Cao, Y. Ma, M. Ma, Second-order adaptive synchrosqueezing S transform and its application in seismic ground roll attenuation. IEEE Geosci. Remote Sens. Lett. 17(8), 1308–1312 (2020)
G. Thakur, H.-T. Wu, Synchrosqueezing-based recovery of instantaneous frequency from nonuniform samples. SIAM J. Math. Anal. 43(5), 2078–2095 (2011)
Q. Wang, J. Gao, N. Liu, X. Jiang, High-resolution seismic time-frequency analysis using the synchrosqueezing generalized S-transform. IEEE Geosci. Remote Sens. Lett. 15(3), 374–378 (2018)
D. Wei, H. Hu, Sparse discrete linear canonical transform and its applications. Signal Process. 183, 108046 (2021)
D. Wei, Y.-M. Li, Generalized sampling expansions with multiple sampling rates for lowpass and bandpass signals in the fractional Fourier transform domain. IEEE Trans. Signal Process. 64(18), 4861–4874 (2016)
D. Wei, Y.-M. Li, Convolution and multichannel sampling for the offset linear canonical transform and their applications. IEEE Trans. Signal Process. 67(23), 6009–6024 (2019)
D. Wei, Y. Shen, Fast numerical computation of two-dimensional non-separable linear canonical transform based on matrix decomposition. IEEE Trans. Signal Process. 69, 5259–5272 (2021)
D. Wei, Y. Zhang, Fractional Stockwell transform: theory and applications. Dig. Signal Process. 115, 103090 (2021)
D. Wei, Y. Zhang, A new fractional wave packet transform. Optik 231, 166357 (2021)
D. Wei, Y. Zhang, Y.-M. Li, Linear canonical stockwell transform: theory and applications. IEEE Trans. Signal Process. 70, 1333–1347 (2022)
X. Xiang-Gen, On bandlimited signals with fractional fourier transform. IEEE Signal Process. Lett. 3(3), 72–74 (1996)
S. Xu, L. Feng, Y. Chai, B. Du, Y. He, Uncertainty relations for signal concentrations associated with the linear canonical transform. Dig. Signal Process. 81, 100–105 (2018)
S. Xu, L. Feng, Y. Chai, Y. He, Analysis of A-stationary random signals in the linear canonical transform domain. Signal Process. 146, 126–132 (2018)
R. Zhang, Z. Wang, Y. Tan, X. Yang, S. Yang, Local maximum frequency-chirp-rate synchrosqueezed chirplet transform. Dig. Signal Process. 130, 103710 (2022)
Z. ZhiChao, New Wigner distribution and ambiguity function based on the generalized translation in the linear canonical transform domain. Signal Process. 118, 51–61 (2016)
Z. Zhichao, Linear canonical Wigner distribution based noisy LFM signals detection through the output SNR improvement analysis. IEEE Trans. Signal Process. 67(21), 5527–5542 (2019)
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grants 61971328 and 62371364, and in part by the Natural Science Basic Research Program of Shaanxi (Program No. 2023-JC-YB-048).
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Appendices
Appendix A
Proof
Firstly, let us calculate FrFT of the kernel function \({\varphi _{\alpha ,a,b}}(t)\).
Secondly, according to Parseval’s theorem of FrFT [27], we have
\(\square \)
Appendix B
Proof
Setting and
, we have

Setting and \({C_\psi } = - \int \limits _0^{ + \infty } {\frac{{\Psi ^* (\zeta )}}{\zeta }} d\zeta \), we have
Then, we can obtain
\(\square \)
Appendix C
According to Fubini’s Theorem and Eq. (14), we have
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Wei, D., Shen, J. Synchrosqueezing Fractional S-transform: Theory, Implementation and Applications. Circuits Syst Signal Process 43, 1572–1596 (2024). https://doi.org/10.1007/s00034-023-02525-w
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DOI: https://doi.org/10.1007/s00034-023-02525-w