Motion Classification Based on Harmonic Micro-Doppler Signatures Using a Convolutional Neural Network

TL;DR

This paper presents a method for classifying motions of held objects using harmonic micro-Doppler signatures captured by harmonic radio-frequency tags and a CNN, achieving over 94% accuracy in real-time within cluttered environments.

Contribution

The study introduces a novel harmonic tag-based radar system combined with a CNN for real-time motion classification, demonstrating high accuracy in complex indoor settings.

Findings

Achieved 94.24% classification accuracy.

Developed scalable harmonic tags at 2.4/4.8 GHz.

Real-time classification with less than 0.5 seconds latency.

Abstract

We demonstrate the classification of common motions of held objects using the harmonic micro-Doppler signatures scattered from harmonic radio-frequency tags. Harmonic tags capture incident signals and retransmit at harmonic frequencies, making them easier to distinguish from clutter. We characterize the motion of tagged handheld objects via the time-varying frequency shift of the harmonic signals (harmonic Doppler). With complex micromotions of held objects, the time-frequency response manifests complex micro-Doppler signatures that can be used to classify the motions. We developed narrow-band harmonic tags at 2.4/4.8 GHz that support frequency scalability for multi-tag operation, and a harmonic radar system to transmit a 2.4 GHz continuous-wave signal and receive the scattered 4.8 GHz harmonic signal. Experiments were conducted to mimic four common motions of held objects from 35 subjects in a cluttered indoor environment. A 7-layer convolutional neural network (CNN) multi-label classifier was developed and obtained a real time classification accuracy of 94.24%, with a response time of 2 seconds per sample with a data processing latency of less than 0.5 seconds.