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.

翻译：本文提出了一种通过评估子样本直到达到任意预设分类精度的方法，从而获得任意精度。随着样本数量线性增加，错误率呈对数级降低。该技术应用于特定辐射源识别，基于一个公开数据集，该数据集包含来自16台外观相同的高性能无线电设备的实际空中物理记录信号。该技术采用多通道深度学习卷积神经网络，作用于I/Q信号子样本的双谱，每个子样本仅占原始信号持续时间的百万分之56（ppm）。在最小计算时间内获得了高精度：在该应用中，每增加八个样本，错误率降低一个数量级。