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Spiking Neural Network-Based Radar Gesture Recognition System Using Raw ADC Data

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posted on 01.03.2022, 05:23 by Muhammad ArsalanMuhammad Arsalan, Avik Santra, Vadim Issakov

One of the main challenges in developing embedded radar-based gesture recognition systems is the requirement of energy efficiency. To facilitate this, we present an embedded gesture recognition system using a 60 GHz frequency modulated continuous wave radar using spiking neural networks (SNNs) applied directly to raw ADC data. The SNNs are sparse in time and space, and event-driven which makes them energy-efficient. In contrast to the previous state-of-the-art methods, the proposed system is only based on the raw ADC data of the target thus avoiding the overhead of performing the slow-time and fast-time Fourier transforms (FFTs). Furthermore, the pre-processing slow-time FFT is mimicked in the proposed SNN architecture, where the proposed model processing speed of 12 ms advances the state-of-the-art by a factor of 17.7. The experimental results demonstrate that despite the simplification the proposed implementation achieves recognition accuracy of 98.1 %, which is comparable to the conventional approaches.

Funding

Electronic Components and Systems for European Leadership Joint Undertaking (Grant No. 826655)

German Federal Ministry of Education and Research (BMBF) (Grant No. 16ES0992K)

History

Email Address of Submitting Author

muhammad.arsalan@infineon.com

Submitting Author's Institution

Infineon Technologies AG

Submitting Author's Country

Germany