RadarCNN: Learning-based Indoor Object Classification from IQ Imaging Radar Data

TL;DR

This paper presents RadarCNN, a machine learning-based indoor object classifier using mmWave radar IQ data, achieving high accuracy and robustness against noise and occlusion.

Contribution

Introduces a novel radar-based indoor object classification system that operates effectively with raw IQ samples, demonstrating robustness without extensive pre-processing.

Findings

Achieves 97-99% accuracy on test data.

Maintains ~50% accuracy under challenging noise and occlusion conditions.

Provides insights into future improvements for generalization.

Abstract

Radar sensors operating in the mmWave frequency range face challenges when used as indoor perception and imaging devices, primarily due to noise and multipath signal distortions. These distortions often impair the sensors' ability to accurately perceive and image the indoor environment. Nevertheless, this sensor offers distinct advantages over camera and LiDAR sensors. This encompasses the estimation of object reflectivity, known as radar cross-section (RCS), and the ability to penetrate through objects that are thin or have low reflectivity. This results in a 'through-the-wall' sensing capability. Due to the aforementioned disadvantages, most research in the field of imaging radar tends to exclude indoor areas. We introduce a machine learning-based mmWave MIMO FMCW imaging radar object classifier designed to identify small, hand-sized objects in indoor settings, utilizing only radar IQ samples as input. This system achieves 97-99 % accuracy on our test set and maintains approximately 50 % accuracy even under challenging conditions, such as increased background noise and occlusion of sample objects, without the need for adjusting training or pre-processing. This demonstrates the robustness of our approach and offers insights into what needs to be improved in the future to achieve generalization and very high accuracy even in the presence of significant indoor perturbations.