Bayesian Program Learning for Modeling and Classification of RF Emitters

Abstract

In this work, we demonstrate an initial application of Bayesian program learning (BPL) to learn models for individual radio frequency (RF) transmitters based on a single training signal for each transmitter. Once learned, these models are used to classify individual RF transmitters based on one signal observation. BPL improves upon other machine learning techniques by learning and classifying effectively from small amounts of training data. BPL programs represent concepts as probabilistic generative models expressed as structured procedures in an abstract description language. These models explicitly account for both concept-specific and context-dependent mechanisms, allowing them to perform well under dynamic environmental conditions. In this ongoing research, we demonstrate our system using signals from a small population of software-defined radios (SDRs) with known signal encodings in a laboratory environment, and provide a path forward for expanding it to larger populations, more signal types, and challenging transmission environments.