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

Abstract

We present the design of narrowband radio-frequency harmonic tags and demonstrate their use in the classification of common motions of held objects using harmonic micro-Doppler signatures. Harmonic tags capture incident signals and retransmit at harmonic frequencies, making them easier to distinguish from clutter. We characterize the motion of tagged, held 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 describe the design of narrow-band harmonic tags at 2.4/4.8 GHz, supporting 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-class classifier was developed that obtained a real time classification accuracy of 94.24$\%$, with a response time of 2 seconds per sample, and with a data processing latency of less than 0.5 seconds.