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Wavelet transform is a time-frequency analysis tool that decomposes a signal into components at different time and frequency scales by scaling and translating a mother wavelet function (e.g., Daubechies wavelet).
Key Features:
1. Multi-scale Analysis: High-frequency parts use narrow windows (capturing details), while low-frequency parts use wide windows (preserving overall trends).
2. Localization: Precisely identifies signal mutations or noise locations (e.g., transient interference in drone signals).
3. Hierarchical Decomposition: The signal is decomposed into approximation components (low-frequency) and detail components (high-frequency), with noise typically concentrated in the high-frequency details.
By decomposing the high/low-frequency components of drone signals, noise is identified and removed (thresholding), and the signal is reconstructed for cleaner TDOA estimation.
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IEEE/ICACT20250181 Slide.05
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