Personal Identification Using Ultrawideband Radar Measurement of Walking and Sitting Motions and a Convolutional Neural Network

TL;DR

This paper introduces a radar-based personal identification method using spectrogram images of walking and sitting motions, processed with a convolutional neural network, achieving promising accuracy for biometric recognition.

Contribution

It presents a novel approach combining ultrawideband radar and CNNs for personal identification based on motion micro-Doppler features.

Findings

High identification accuracy demonstrated experimentally

Effective use of spectrogram images for biometric recognition

Potential for non-contact, privacy-preserving identification systems

Abstract

This study proposes a personal identification technique that applies machine learning with a two-layered convolutional neural network to spectrogram images obtained from radar echoes of a target person in motion. The walking and sitting motions of six participants were measured using an ultrawideband radar system. Time-frequency analysis was applied to the radar signal to generate spectrogram images containing the micro-Doppler components associated with limb movements. A convolutional neural network was trained using the spectrogram images with personal labels to achieve radar-based personal identification. The personal identification accuracies were evaluated experimentally to demonstrate the effectiveness of the proposed technique.