Micro-Doppler Based Human-Robot Classification Using Ensemble and Deep Learning Approaches
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Radar sensors can be used for analyzing the induced frequency shifts due to micro motions in both range and velocity dimensions identified as micro-Doppler (μ-D) and micro-Range (μ-R) respectively. Different moving targets will have unique μ-D and μ-R signatures that can be used for target classification. Such classification can be used in numerous fields such as gait recognition, safety and surveillance. In this paper, a [25]GHz FMCW Single Input Single Output (SISO) radar is used in industrial safety for real-time human-robot identification. Due to the real-time constraint, joint Range-Doppler (R-D) maps are directly analyzed for our classification problem. Furthermore, a comparison between the conventional classical learning approaches with handcrafted extracted features, ensemble classifiers and deep learning approaches is presented. For ensemble classifiers, a restructured range and velocity profiles are passed directly to ensemble trees such as gradient boosting and random forest without feature extraction. Finally, a Deep Convolutional Neural Network (DCNN) is used and raw R-D images are directly fed to the constructed network. DCNN shows a superior performance of 99% accuracy in identifying humans from robots on a single R-D map.
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