A Real-World Dataset Generator for Specific Emitter Identification
Braeden P. Muller, Lauren J. Wong, William H. Clark, Alan J. Michaels · IEEE Access · 2023
Generating high-quality, real-world, well-labeled datasets for radio frequency machine learning (RFML) applications often proves prohibitively cumbersome and expensive, leading to the lowavailability of high-fidelity, low-cost datasets. Specific emitter identification (SEI) in particular requires a hardware setup capable of supporting transmitting using many different radios, while automated modulation classification (AMC) performance is primarily driven by SNR, channel effects, and the similarity of modulation types. These factors give rise to the need for scalable methods of inexpensive dataset generation. This paper describes the design considerations and a proof-of-concept implementation of a blind user reconfigurable platform capable of creating SEI and AMC datasets throughout a variety of real-world conditions. This paper additionally describes the reliability and performance of the platform relative to existing real-world data generation methods and compares generated datasets to those already present in the literature. This work also describes the software post-processing steps taken to isolate, label, and cull captured data and transform these into a high-quality dataset.