An EEMD-based LSTM method for reconstructing the attenuated interference signals in a laser doppler vibrometry system

Abstract

Speckle-induced signal dropouts are a persistent challenge in Laser Doppler Vibrometry (LDV), degrading the accuracy of velocity measurements. This paper proposes a framework with an ensemble empirical mode decomposition (EEMD)-based long short-term memory (LSTM) to address this issue by reconstructing the attenuated interference signals that cause these dropouts. The core of our framework is a two-stage process. First, the attenuated interference signal is decomposed into intrinsic mode functions (IMFs) using EEMD, selectively weighted, and then recombined to generate a non-attenuated interference signal. Second, an LSTM network is trained to learn this entire transformation. It acts as a computationally efficient model that maps the attenuated input signal directly to the reconstructed output signal. The performance of the proposed EEMD-based LSTM method was rigorously verified using both a software simulator and a hardware-in-the-loop experiment with an analog circuit. The results confirm that our method effectively reduces the velocity signal dropouts in both simulation and experimental settings, demonstrating its significant potential for improving the reliability of LDV systems.