Comparative Analysis of RAMP-CNN and RADICAL-CNN Methods for Object Classification with FMCW Radar Data: A Case Study on Radical Dataset

This paper investigates two approaches for real-time object detection and classification using Frequency-Modulated Continuous-Wave (FMCW) radar data from the Radical Dataset. The first method uses the Constant False Alarm Rate (CFAR) algorithm and manual annotations to extract patches from Range-Azimuth (RA) heatmaps, which are then processed by a simple Convolutional Neural Network (CNN) named RADICAL-CNN. The second approach evaluates the performance of the RAMP-CNN model on the Radical Dataset, which incorporates RA, Range-Doppler (RD) and Doppler-Azimuth (DA) data and utilizes 3D convolution to integrate temporal information. This comparative analysis of two innovative neural network architectures not only highlights their strengths and weaknesses, but also contributes to the ongoing optimization of object detection techniques for FMCW radar data in dynamic, real-world scenarios such as autonomous driving, robotics, zone monitoring and safety.