The problem of detecting chirps is present in many applications of Signal Processing. Proper denoising, which involves filtering the signals after their acquisition, improves the efficacy of their detection. This manuscript describes how a recently-published method of Time-Frequency Analysis (TFA) with reassignment, namely the Newton Time-Extracting Wavelet Transform (NTEWT), can be employed as a highly-performing chirp filter. The proposed methodology has the advantage of denoising chirps without distorting their instantaneous phases, as linear convolutional filters do. Numerical experiments have proven the efficacy of the proposed filter. After NTEWT-based filtering, the resolution of chirp detection with matched filtering is notably improved, even when the signals contain white noise. The computation times of the proposed numerical implementation of the NTEWT are lower than those reported in its seminar paper.

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