Multi-Frequency RF Sensor Data Adaptation for Motion Recognition with Multi-Modal Deep Learning

Abstract

The widespread availability of low-cost RF sensors has made it easier to construct RF sensor networks for motion recognition, as well as increased the availability of RF data across a variety of frequencies, waveforms, and transmit parameters. However, it is not effective to directly use disparate RF sensor data for the training of deep neural networks, as the phenomenological differences in the data result in significant performance degradation. In this paper, we consider two approaches for the exploitation of multi-frequency RF data: 1) a single sensor case, where adversarial domain adaptation is used to transform the data from one RF sensor to resemble that of another, and 2) a multi-sensor case, where a multi-modal neural network is designed for joint target recognition using measurements from all sensors. Our results show that the developed approaches offer effective techniques for leveraging multi-frequency RF sensor data for target recognition.

Description
Keywords
micro-Doppler, radar, multi-modal learning, adversarial neural networks
Citation
Rahman, M., Gurbuz, S. (2021): Multi-Frequency RF Sensor Data Adaptation for Motion Recognition with Multi-Modal Deep Learning. Paper presented at 2021 IEEE Radar Conference: Radar on the Move.