On the Integration of Wideband Adaptable Hardware Technologies to Enable RF Machine Learning
Conference
Livadaru, M, Redenbaugh, A, McMahon, B et al. (2019). On the Integration of Wideband Adaptable Hardware Technologies to Enable RF Machine Learning
. 2019-October 10.1109/PAST43306.2019.9020720
Livadaru, M, Redenbaugh, A, McMahon, B et al. (2019). On the Integration of Wideband Adaptable Hardware Technologies to Enable RF Machine Learning
. 2019-October 10.1109/PAST43306.2019.9020720
In this paper, we summarize our hardware implementation for the DARPA Radio Frequency Machine Learning Systems (RFMLS) program. The system combines our wideband antenna array with past DARPA's investments in wideband technologies: BAE Systems' Hedgehog board with monolithic RF-to-baseband transceivers and Xilinx's RFSoC, a multi-processor, multi-converter FPGA. This combination of proven wideband technologies creates a powerful multi-channel software defined radio (SDR) controlled via an industry standard open interface. Measured active beam patterns of the digital array are also presented here to validate our hardware approach.
