Motion Classification Based on Harmonic Micro-Doppler Signatures Using a Convolutional Neural Network
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...
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Zusammenfassung: | 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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DOI: | 10.48550/arxiv.2301.05652 |