Publisher DOI: 10.1109/JSEN.2021.3092002
Title: Data-driven Radar Processing Using a Parametric Convolutional Neural Network for Human Activity Classification
Language: English
Authors: Stadelmayer, Thomas 
Santra, Avik 
Weigel, Robert 
Lurz, Fabian 
Keywords: 2D Sinc Filters;2D Wavelets;Chirp;Deep Learning;Doppler effect;Doppler radar;Feature extraction;Human Activity Classification;Parametric CNN;Radar;Sensors;Spectrogram
Issue Date: 1-Sep-2021
Source: IEEE Sensors Journal 21 (17): 19529-19540 (2021-09-01)
Journal: IEEE sensors journal 
Abstract (english): 
The paper proposes a data-driven pre-processing optimization for radar data using a parametric convolutional neural network. The proposed method is applied on human activity classification as a use case. Present radar-based activity recognition system exploit micro-Doppler signature by generating Doppler spectrograms or a temporal series of range-Doppler maps, followed by deep neural networks or machine learning approaches for classification. Those radar data representations are typically generated on the basis of short-time Fourier transformations. A Fourier transformation equally resolves the frequency space, which may be sub-optimal in some applications. Although deep convolutional neural networks (DCNN) have been shown to implicitly learn features from raw sensor data in other fields, such as speech recognition, yet, for the case of radar-based DCNNs, pre-processing is required to develop a scalable and robust classification or regression application. In this paper, we propose a parametric convolutional neural network that mimics the radar pre-processing across fast-time and slow-time radar data through 2D sinc filter or 2D wavelet filter kernels to extract features for classification of various human activities. During training only the filter parameters of the 2D sinc filters or 2D wavelets are learned, leading to optimized feature representation for the classification task. It is demonstrated that our proposed solution shows improved results compared to equivalent DCNN architectures that rely on Doppler spectrograms or radar data cubes as input data.
ISSN: 1558-1748
Institute: Hochfrequenztechnik E-3 
Document Type: Article
Peer Reviewed: Yes
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