Multimode Fiber Specklegram Sensor for Multi-Position Loads Recognition Using Traversal Occlusion

Since an MMF-based distributed sensor requires the simultaneous measurement of multiple perturbation positions and their intensities, the collection of a large amount of specklegram data is time consuming and challenging for recognizing multiple perturbations. To address this issue, we propose a novel approach to recognize multi-position load using an MMF specklegram sensor, supported by theoretical analysis and experimental verification. Our study introduces a construction method for a multi-variable, multi-class, one-shot specklegram dataset, significantly enhancing the sample diversity for more perturbation positions and intensities in an MMF-distributed sensor recognition model. We theoretically derive the mathematical model of total local intensity for each region and investigate its sensitivity to the external perturbations. Based on these theoretical analyses, this paper proposes a specklegram traversal occlusion data augmentation with a shallow convolutional neural network (CNN) model to mitigate overfitting in specklegram datasets. Experimental validation using a multi-position load-recognition MMF demonstrates that our approach achieves nearly 100% accuracy in simultaneously recognized load positions and its magnitudes across up to 1545 distinct load forms. Furthermore, the shallow CNN model exhibits superior training efficiency and stability compared with the existing MMF sensing models. This work provides a proof of concept of a distributed sensor based on an MMF specklegram sensor, highlighting its potential for high-resolution distributed measurements under the diverse external perturbations. Our method represents a significant advancement in this field, offering a cost-effective and efficient solution for distributed sensing applications.