Lightweight, Deep RNNs for Radar Classification

Abstract

We demonstrate Multi-Scale, Cascaded RNN (MSC-RNN)1, an energy-efficient recurrent neural network for real-time micro-power radar classification. Its two-tier architecture is jointly trained to reject clutter and discriminate displacing sources at different time-scales, with a lighter lower tier running continuously and a heavier upper tier invoked infrequently on an on-demand basis. It offers for single microcontroller devices a better trade-off in accuracy and efficiency, as well as in clutter suppression and detectability, over competitive shallow and deep alternatives.