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.
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