Deep Models for Peak Finding with FMCW Radar Depth Estimation Applications

This article describes deep learning models for peak detection for FMCW radar depth estimation. Peak detection is a common problem for many fields, where in radar it is used for distance and speed estimation by analyzing the in-phase and quadrature components of the signal. Traditional methods for peak detection focus on local maxima and local derivative information to determine the location of a peak, however, because of this it is fine-tuned for specific peak shapes. We seek to approach this problem using a custom deep neural network to find peaks of various shapes and sizes that would not be found using traditional methods. Using this approach, it is demonstrated that it can outperform commonly used peak detection algorithms such as Scipy's find_peaks. For this article, we have: (1) a new AI algorithm for peak detection for FMCW radar depth estimation, (2) viewed the problem as a regression vs classification problem, and (3) Considered the pros and con of multiple loss functions for both problems.