Flexible Compression for Efficient Information Sharing in a Network of Radio Frequency Sensors

The efficient extraction of useful information from radio frequency (RF) sensors is one important application for artificial intelligence (AI) and machine learning (ML) approaches. In particular, there is a desire to maximize efficiency when sharing positioning, navigation, and timing (PNT) information captured by distributed networks of low size, weight, power, and cost (SWAP-C) RF sensors when operating in congested or contested electromagnetic environments (EMEs). By implementing effective PNT information-sharing strategies, these networks can more easily position the sensors or characterize targets of interest. In this work, we propose a novel ML-inspired compression design framework that improves efficiency when sharing PNT information in a network of sensors receiving radar waveforms. In addition, through novel learning procedures, the network can adapt to unforeseen EMEs such that network efficiency can be maintained in the presence of unforeseen RF waveforms and sensor surroundings. We show that our intelligent, model-driven, ML-inspired data reduction strategies can outperform alternative strategies that do not best-utilize the information content of waveforms in the EME. In addition, we demonstrate the ability of our strategies to adapt to changing mission goals by balancing different types of PNT information and learning from developing EMEs.