  This article introduces a method of evaluating subsamples until any
prescribed level of classification accuracy is attained, thus obtaining
arbitrary accuracy. A logarithmic reduction in error rate is obtained with a
linear increase in sample count. The technique is applied to specific emitter
identification on a published dataset of physically recorded over-the-air
signals from 16 ostensibly identical high-performance radios. The technique
uses a multi-channel deep learning convolutional neural network acting on the
bispectra of I/Q signal subsamples each consisting of 56 parts per million
(ppm) of the original signal duration. High levels of accuracy are obtained
with minimal computation time: in this application, each addition of eight
samples decreases error by one order of magnitude.
