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arXiv · 2301.05652

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

Cory Hilton·Steve Bush·Faiz Sherman·Matt Barker·Aditya Deshpande·Steve Willeke·Jeffrey A. Nanzer
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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.

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Cory Hilton, Steve Bush, Faiz Sherman, Matt Barker, Aditya Deshpande, Steve Willeke, Jeffrey A. Nanzer. 2023-01-13. Motion Classification Based on Harmonic Micro-Doppler Signatures Using a Convolutional Neural Network. https://arxiv.org/abs/2301.05652

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