A novel synchrosqueezing transform to the time-frequency representation based on short-time fractional Fourier transform for strong time-varying signals is proposed in this paper. To finely describe the local characteristics of nonstationary signal in the short-time fractional Fourier transform domain, a new instantaneous frequency estimator, a new instantaneous fractional frequency estimator and the corresponding energy reassignment operators are given by time-fractional frequency basis. The discrete implementation and signal reconstruction of the proposed method are investigated. Furthermore, the calculation of the optimal fractional order, computational complexity and limitation of the proposed method are discussed. Various strong time-varying signal experiments and time-frequency spectrum energy attenuation gradient extraction experiments of real synthetic aperture radar (SAR) images are conducted to verify the effectiveness and superiority of the proposed method over some advanced time-frequency analysis methods in energy concentration, time-frequency localization, signal reconstruction and clutter suppression.

Synchrosqueezing-Based Short-Time Fractional Fourier Transform

A novel synchrosqueezing transform to the time-frequency representation based on short-time fractional Fourier transform for strong time-varying signals is proposed in this paper. To finely describe the local characteristics of nonstationary signal in the short-time fractional Fourier transform domain, a new instantaneous frequency estimator, a new instantaneous fractional frequency estimator and the corresponding energy reassignment operators are given by time-fractional frequency basis. The discrete implementation and signal reconstruction of the proposed method are investigated. Furthermore, the calculation of the optimal fractional order, computational complexity and limitation of the proposed method are discussed. Various strong time-varying signal experiments and time-frequency spectrum energy attenuation gradient extraction experiments of real synthetic aperture radar (SAR) images are conducted to verify the effectiveness and superiority of the proposed method over some advanced time-frequency analysis methods in energy concentration, time-frequency localization, signal reconstruction and clutter suppression. 

In the application of the matched field processing (MFP) algorithm for underwater acoustic source localization, the measurements at each time step are conventionally processed independently. This study incorporates the prior information about the continuous spatial changes of the source over time under realistic conditions, a factor anticipated to improve localization performance. In this paper, a sparse Bayesian learning (SBL) algorithm based on the spatio-temporal structure-aware is described. We exploit a structure prior for sparse coefficients to capture the continuous spatial structure between adjacent time steps. Moreover, the sparse coefficient can automatically select the update method, utilizing the statistical information from adjacent neighbors or updating independently. The hidden variables in the hierarchical Bayesian framework are inferred via variational Bayesian inference (VBI). Additionally, we extend the proposed method to the multi-frequency case. This method inherits the advantages of the SBL and further reduces position estimation errors. Compared to other approaches, the construction of an accurate motion model is not required. The efficacy of the proposed algorithm is demonstrated through simulation examples and an analysis of the SWellEx-96 experimental data.

Sparse Bayesian learning based on spatio-temporal structure-aware for matched field processing.

Wavelet-Based Spatiotemporal Sparse Quaternion Dictionary Learning for Reconstruction of Multi-Channel Vibration Data

Adaptive Cluster Structured Sparse Bayesian Learning with Application to Compressive Reconstruction for Chirp Signals

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Adaptive Cluster Structured Sparse Bayesian Learning with Application to Compressive Reconstruction for Chirp Signals