Robust Doppler-Based Gesture Recognition With Incoherent Automotive Radar Sensor Networks
Nicolai Kern, Maximilian Steiner, Ramona Lorenzin, Christian Waldschmidt
Abstract
In this letter, the capabilities of an incoherent radar sensor network for robust Doppler-based gesture recognition are investigated, and a significant performance boost is demonstrated. A comprehensive dataset is recorded with an incoherent sensor network consisting of three time-synchronized 77GHz frequency-modulated continuous wave radars. Based on this dataset, we show that differential Doppler features obtained from the varying viewing angles result in a significant multistatic gain for classification, particularly for high intraclass variations and low Doppler frequencies. For the most complex dataset, cross-user validation accuracy of a convolutional neural network with optimized data fusion is improved by 7.4% to an overall value of 87.1%, which we regard to be high as gestures are not designed for distinguishability but reflect everyday control and communication signals.