  We present a new RF fingerprinting technique for wireless emitters that is
based on a simple, easily and efficiently retrainable Ridge Regression (RR)
classifier. The RR learns to identify devices using bursts of waveform samples,
conveniently transformed and preprocessed by delay-loop reservoirs. Deep delay
Loop Reservoir Computing (DLR) is our processing architecture that supports
general machine learning algorithms on resource-constrained devices by
leveraging delay-loop reservoir computing (RC) and innovative architectures of
loop trees. In prior work, we trained and evaluated DLR using high SNR device
emissions in clean channels. We here demonstrate how to use DLR for IoT
authentication by performing RF-based Specific Emitter Identification (SEI),
even in the presence of fading channels and heavy in-band jamming by leveraging
a matched filter (MF) extension, dubbed MF-DLR. We show that the MF processing
improves the SEI performance of RR without the RC transformation (MF-RR), but
the MF-DLR is more robust and applicable for addressing signatures beyond
waveform transients (e.g. turn-on).
