论文标题
任意准确的分类应用于特定的发射极标识
Arbitrarily Accurate Classification Applied to Specific Emitter Identification
论文作者
论文摘要
本文介绍了一种评估子样本的方法，直到达到任何规定的分类准确性，从而获得任意准确性。随着样品计数线性增加，获得了错误率的对数降低。该技术应用于从16个表面上相同的高性能收音机的物理记录的空中信号的数据集上的特定发射极标识。该技术使用由I/Q信号子样本的Bispectra进行的多通道深度学习卷积神经网络，每个神经网络由原始信号持续时间的百万分之56（ppm）组成。使用最小的计算时间获得了高度的准确性：在此应用中，每次添加八个样本都会减少一个数量级的误差。
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.
