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 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.
Code (0)
등록된 구현이 없습니다.
Tasks
TAGSimilar Papers 제목 키워드 기반
Synthesis of Through-Wall Micro-Doppler Signatures of Human Motions Using Generative Adversarial Networks
Narrowband radar micro-Doppler signatures are heavily used to identify and classify human activities. When the radar is operated in through-wall environments, the complex electromagnetic propagation phenomenology introdu…
DenoisingGenerative Adversarial NetworkDNN Transfer Learning from Diversified Micro-Doppler for Motion Classification
Recently, deep neural networks (DNNs) have been the subject of intense research for the classification of radio frequency (RF) signals, such as synthetic aperture radar (SAR) imagery or micro-Doppler signatures. However,…
Activity RecognitionHuman Activity RecognitionTransfer LearningA Multi-Characteristic Learning Method with Micro-Doppler Signatures for Pedestrian Identification
The identification of pedestrians using radar micro-Doppler signatures has become a hot topic in recent years. In this paper, we propose a multi-characteristic learning (MCL) model with clusters to jointly learn discrepa…
Radar Human Motion Classification Using Multi-Antenna System
This paper considers human activity classification for an indoor radar system. Human motions generate nonstationary radar returns which represent Doppler and micro-Doppler signals. The time-frequency (TF) analysis of mic…
BIG-bench Machine LearningClassificationGeneral ClassificationMotion Classification using Kinematically Sifted ACGAN-Synthesized Radar Micro-Doppler Signatures
Deep neural networks (DNNs) have recently received vast attention in applications requiring classification of radar returns, including radar-based human activity recognition for security, smart homes, assisted living, an…
Activity RecognitionDiversityGeneral ClassificationHuman Activity Recognition