Time-Frequency Signal Analysis Techniques
Summary
Time-frequency signal analysis techniques furnish a unified framework for examining non-stationary and multicomponent signals by representing their energy distribution jointly in time and frequency. Core methods include the short-time Fourier transform, which applies a sliding window to capture local spectral content, and the wavelet transform, which offers multi-resolution analysis suited to signals with transient features. Quadratic time-frequency distributions, such as the Wigner–Ville distribution, provide high resolution but may introduce cross-term artefacts that require suppression through kernel design or reassignment approaches. More recent advances encompass synchrosqueezing and synchroextracting transforms, which sharpen time-frequency ridges to facilitate instantaneous frequency estimation and mode decomposition. These tools underpin applications across biomedical signal processing, radar and sonar, telecommunications, geophysics and structural monitoring. By balancing time and frequency resolution, mitigating interference among components and enabling automated mode separation, time-frequency analysis remains central to the characterisation and extraction of signal features in complex and noisy environments.
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Time-Frequency Signal Analysis Techniques publication trend
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Technical terms
Time-frequency distribution: A representation that maps signal energy jointly in time and frequency, revealing how spectral content evolves over time.
Short-Time Fourier Transform (STFT): A linear transform obtained by applying the Fourier transform to windowed segments of a signal, balancing time and frequency resolution via window length.
Wavelet Transform: A multi-resolution transform using scaled and translated prototypes (wavelets) to analyse signals at varying time and frequency scales, well suited to transient detection.
Instantaneous Frequency (IF): The time-varying rate of phase change of a signal component, representing its local spectral centre and guiding mode separation.
Radon Transform: An integral transform that projects a two-dimensional function along lines at various angles, used to detect linear features such as time-frequency ridges for component extraction.
References
- Area-Efficient Short-Time Fourier Transform Processor for Time–Frequency Analysis of Non-Stationary Signals. Applied Sciences (2020).
- Radon spectrogram-based approach for automatic IFs separation. EURASIP Journal on Advances in Signal Processing (2020).
- An Efficient Direction of Arrival Estimation Algorithm for Sources with Intersecting Signature in the Time–Frequency Domain. Applied Sciences (2021).
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