Motion Classification Based on Harmonic Micro-Doppler Signatures Using a Convolutional Neural Network
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arXiv
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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866914115486744576 |
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| author | Hilton, Cory Bush, Steve Sherman, Faiz Barker, Matt Deshpande, Aditya Willeke, Steve Nanzer, Jeffrey A. |
| author_facet | Hilton, Cory Bush, Steve Sherman, Faiz Barker, Matt Deshpande, Aditya Willeke, Steve Nanzer, Jeffrey A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2301_05652 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Motion Classification Based on Harmonic Micro-Doppler Signatures Using a Convolutional Neural Network Hilton, Cory Bush, Steve Sherman, Faiz Barker, Matt Deshpande, Aditya Willeke, Steve Nanzer, Jeffrey A. Signal Processing Image and Video Processing 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. |
| title | Motion Classification Based on Harmonic Micro-Doppler Signatures Using a Convolutional Neural Network |
| topic | Signal Processing Image and Video Processing |
| url | https://arxiv.org/abs/2301.05652 |