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.

翻译：在信号处理的诸多应用中，啁啾检测问题普遍存在。通过信号采集后的滤波进行适当降噪，可提高其检测效果。本文描述了如何将近期发表的带重排时频分析方法——牛顿时间提取小波变换，用作高性能啁啾滤波器。该方法具有在不扭曲瞬时相位的情况下对啁啾信号进行降噪的优势，这是线性卷积滤波器无法实现的。数值实验证明了该滤波器的有效性。经基于NTEWT滤波处理后，即便信号包含白噪声，采用匹配滤波的啁啾检测分辨率也得到显著提升。本文所提出的NTEWT数值实现的计算时间低于其开创性论文中报告的值。