Time-frequency analysis using sidelobe apodization
Gabriel Thomas
Abstract
Gabriel Thomas
Abstract
Time-frequency techniques have been successfully used in the analysis of non-stationary signals. Several approaches have been proposed that address concerns such as Time-Frequency (TF) resolution and the elimination of cross-terms. In this work, a TF technique based on the use of Spatially Variant Apodization (SVA) is introduced that focuses on the detection of non-stationary signals that consists of several components that have different amplitudes. The SVA approach is applied to the Short-Time Fourier Transform (STFT) to detect small intensity components that are buried in high sidelobes of other components. Resolution using the SVA is better than the resolution obtained using the STFT with non-rectangular windows. Synthesis can be performed using the overlap-add method. Because of the implementation of the SVA, the modified STFT using sidelobe apodization can have good resolution, detect small intensity components, and show no cross terms in the TF plane, given that stationarity can be assumed using an appropriate window length in the STFT.
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Time-frequency techniques have been successfully used in the analysis of non-stationary signals. Several approaches have been proposed that address concerns such as Time-Frequency (TF) resolution and the elimination of cross-terms. In this work, a TF technique based on the use of Spatially Variant Apodization (SVA) is introduced that focuses on the detection of non-stationary signals that consists of several components that have different amplitudes. The SVA approach is applied to the Short-Time Fourier Transform (STFT) to detect small intensity components that are buried in high sidelobes of other components. Resolution using the SVA is better than the resolution obtained using the STFT with non-rectangular windows. Synthesis can be performed using the overlap-add method. Because of the implementation of the SVA, the modified STFT using sidelobe apodization can have good resolution, detect small intensity components, and show no cross terms in the TF plane, given that stationarity can be assumed using an appropriate window length in the STFT.
Key concepts: Apodization, Short-time Fourier transform, Computer science, Time–frequency analysis, Fourier transform, Resolution (logic), Window function, Algorithm