Rapid parameter estimation of discrete decaying signals using autoencoder networks

TL;DR

This paper presents a neural network-based method using autoencoders for rapid and accurate parameter estimation of discretely sampled decaying signals, significantly outperforming traditional algorithms in speed while maintaining similar precision.

Contribution

The authors introduce a three-stage training autoencoder approach for fast signal parameter extraction, achieving analysis rates of 75 kHz and potential for over 200 kHz with further optimization.

Findings

Autoencoders can extract signal parameters with precision near the Cramér-Rao bound.

The method achieves analysis speeds of 75 kHz, much faster than conventional techniques.

Potential to reach over 200 kHz with system optimization.

Abstract

In this work we demonstrate the use of neural networks for rapid extraction of 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. By 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 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 similar 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.