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:本条引入了一种在达到任何规定的分类准确度之前评价子样本的方法,从而获得任意的准确性。随着样本数的线性增加,对误差率进行了对数减少。该技术适用于在16个表面上相同的高性能无线电台实际记录在空中信号的数据集中的具体发射识别。该技术使用一个多通道深层学习神经神经网络,在I/Q信号分样的双谱上运行,每分数由最初信号时间的每百万分之56(ppm)组成。在最短的计算时间内获得高度准确性:在这一应用中,每增加8个样本,就会减少一个数量级的错误。