Laser self-mixing interference micro displacement reconstruction based on convolutional neural network

Li Xintao; Liu Hui; Qiao Shuo; Yang Yifan; Lv Yang; Liu Xia; Xiong Lingling

doi:10.11884/HPLPB202638.250370

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Article Navigation > High Power Laser and Particle Beams > 2026 > Accepted Manuscript

Li Xintao, Liu Hui, Qiao Shuo, et al. Laser self-mixing interference micro displacement reconstruction based on convolutional neural network[J]. High Power Laser and Particle Beams. doi: 10.11884/HPLPB202638.250370

Citation:

Li Xintao, Liu Hui, Qiao Shuo, et al. Laser self-mixing interference micro displacement reconstruction based on convolutional neural network[J]. High Power Laser and Particle Beams. doi: 10.11884/HPLPB202638.250370

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Laser self-mixing interference micro displacement reconstruction based on convolutional neural network

doi: 10.11884/HPLPB202638.250370

School of Mechanical and Electrical Engineering, Xi'an Polytechnic University, Xi'an 710048, China

Received Date: 2025-10-28
Accepted Date: 2025-12-30
Rev Recd Date: 2025-12-30

Available Online: 2026-02-03

Abstract

Abstract

Background
Laser self-mixing interferometry (SMI) is a highly sensitive and non-contact technique widely used for micro-displacement measurement. However, traditional displacement reconstruction methods typically involve complex phase unwrapping calculations, which increases computational difficulty and limits the efficiency of signal processing in practical applications.
Purpose
This study aims to propose a novel micro-displacement reconstruction method for semiconductor laser SMI based on convolutional neural networks (CNN). The objective is to achieve direct and accurate reconstruction of micron-scale displacement while bypassing the tedious phase unwrapping process.
Methods
The proposed method involves segmenting the SMI signal and using the window-averaged displacement as the label for training the CNN. The architecture of the network consists of three sets of convolutional layers, pooling layers, and Rectified Linear Unit (ReLU) functions. Specifically, the convolutional layers are utilized to extract local displacement features from the SMI signal, the pooling layers are designed to compress feature information and enhance noise immunity, and the ReLU functions help highlight critical displacement features within the signal.
Results
In theoretical simulations, SMI signals with 10 dB noise were input into the trained CNN, resulting in a displacement reconstruction RMSE of 5.3 × 10⁻⁸. In experimental tests, SMI signals containing system noise were processed by the network, yielding a reconstructed displacement RMSE of 2.1 × 10⁻⁷. The simulation and experimental results demonstrate consistent performance.
Conclusions
Both theoretical and experimental results indicate that the convolutional neural network can effectively achieve micron-level displacement reconstruction by analyzing the temporal segments of SMI signals. This method provides an efficient alternative for semiconductor laser self-mixing interference systems by eliminating the need for complex phase-based algorithms.
- laser self-mixing interference,
- displacement reconstruction,
- convolutional neural network,
- feature extraction,
- semiconductor laser

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References(29)

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