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http://dx.doi.org/10.5515/KJKIEES.2018.29.7.550

Human Walking Detection and Background Noise Classification by Deep Neural Networks for Doppler Radars  

Kwon, Jihoon (Radar R&D Center, Hanwha Systems)
Ha, Seoung-Jae (Department of Information and Communication Systems, Korea Polytechnics)
Kwak, Nojun (Graduate School of Convergence Science and Technology, Seoul National University)
Publication Information
The Journal of Korean Institute of Electromagnetic Engineering and Science / v.29, no.7, 2018 , pp. 550-559 More about this Journal
Abstract
The effectiveness of deep neural networks (DNNs) for detection and classification of micro-Doppler signals generated by human walking and background noise sources is investigated. Previous research included a complex process for extracting meaningful features that directly affect classifier performance, and this feature extraction is based on experiences and statistical analysis. However, because a DNN gradually reconstructs and generates features through a process of passing layers in a network, the preprocess for feature extraction is not required. Therefore, binary classifiers and multiclass classifiers were designed and analyzed in which multilayer perceptrons (MLPs) and DNNs were applied, and the effectiveness of DNNs for recognizing micro-Doppler signals was demonstrated. Experimental results showed that, in the case of MLPs, the classification accuracies of the binary classifier and the multiclass classifier were 90.3% and 86.1%, respectively, for the test dataset. In the case of DNNs, the classification accuracies of the binary classifier and the multiclass classifier were 97.3% and 96.1%, respectively, for the test dataset.
Keywords
Doppler Radar; Deep Neural Network; Micro-Doppler; Radar Pattern Recognition; Radar Machine Learning;
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