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Adaptive Multi-source Separation Strategy For A Distributed Optical Fiber Sensor Based On A Mask Network.

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Adaptive Multi-source Separation Strategy For A Distributed Optical Fiber Sensor Based On A Mask Network.

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Adaptive multi-source separation strategy for a distributed optical fiber sensor based on a mask network.

Yi Shi, Jie Chen, Qiren Yan

|December 19, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces an adaptive strategy to separate overlapping events in fiber optic sensing systems, even with limited data. The method refines existing signals to improve event isolation in phase-sensitive optical time-domain reflectometry (φ-OTDR) systems.

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Area of Science:

Optical Engineering
Signal Processing
Sensing Technology

Background:

Separating overlapping multi-source events in φ-OTDR systems is challenging, especially with limited training data.
Data scarcity can cause overfitting in mask estimators, hindering the isolation of desired signal components.
Conventional methods often rely on data augmentation to address limited datasets.

Purpose of the Study:

To propose a novel adaptive separation strategy for multi-source events in φ-OTDR systems.
To overcome the limitations of data scarcity and improve the separation of overlapping signals.
To enhance the quality of separated results without extensive data augmentation.

Main Methods:

Developed an adaptive separation strategy that iteratively refines poorly separated signals.

Leveraged existing datasets and mask estimators within the iterative process.

Validated the approach through theoretical analysis and experimental testing.

Main Results:

The adaptive strategy effectively separates overlapping multi-source events in φ-OTDR systems.
Demonstrated improved signal isolation quality under data-limited conditions.
Showcased the feasibility and portability of the strategy across various scenarios.

Conclusions:

The proposed adaptive separation strategy offers a novel solution for multi-source event separation in φ-OTDR.
The method is effective even with limited training samples, addressing a key challenge in the field.
The strategy shows promising potential for practical applications in optical sensing.