In this work we demonstrate the use of autoencoder networks for rapid extraction of the signal parameters of discretely sampled signals. In particular, we use dense autoencoder networks to extract the parameters of interest from exponentially decaying signals and decaying oscillations. Using a three-stage training method and careful choice of the neural network size, we are able to retrieve the relevant signal parameters directly from the latent space of the autoencoder network at significantly improved rates compared to traditional algorithmic signal-analysis approaches. We show that the achievable precision and accuracy of this method of analysis is similar to conventional, algorithm-based signal analysis methods, by demonstrating that, the extracted signal parameters are approaching their fundamental parameter estimation limit as provided by the Cram\'er-Rao lower bound. Furthermore, we demonstrate that autoencoder networks are able to achieve signal analysis, and, hence, parameter extraction, at rates of 75 kHz, orders-of-magnitude faster than conventional techniques with equal precision. Finally, we explore the limitations of our approach, demonstrating that analysis rates of $>$200 kHz are feasible with further optimization of the transfer rate between the data-acquisition system and data-analysis system.
翻译:在这项工作中,我们展示了使用自动编码器网络迅速提取离散抽样信号信号参数的自动编码器网络;特别是,我们使用密度大的自动编码器网络从指数衰减信号和振荡信号中提取引力参数;使用三阶段培训方法和仔细选择神经网络大小,我们能够直接从自动编码器网络的潜藏空间中检索相关的信号参数,其速度比传统算法信号分析方法高得多;我们表明,这种分析方法的可实现精确性和准确性与常规的、基于算法的信号分析方法相似,我们通过证明提取的信号参数接近Cram\'er-Rao较低约束线提供的基本参数估计限值;此外,我们证明,自动编码器网络能够以75千赫的速度进行信号分析,因此,参数的提取速度比常规技术的精确速度快得多;我们探索了我们方法的局限性,表明,在数据采集和数据系统之间对数据采集率进行进一步优化的情况下,超过200千赫兹的分析是可行的。