Abstract

Fiber optic inline interferometers are widely used for high-precision sensing due to their sensitivity, compactness, and immunity to electromagnetic interference. Traditional optical spectral analysis methods suffer from limited dynamic range due to free spectral range (FSR) constraints, while microwave photonic filtering (MPF) techniques based on dispersion Fourier transform (DFT) provide an alternative by mapping optical signals into the radio frequency (RF) domain. However, conventional passband frequency tracking in MPF systems has limited sensitivity, and the recently demonstrated phase-based methods, though highly sensitive, are constrained by phase wrapping beyond 2π. In this work, we propose and experimentally demonstrate an integrated magnitude–phase analysis strategy to overcome these limitations. By simultaneously utilizing both magnitude and phase responses in a single MPF measurement, our approach achieves high sensitivity while extending the dynamic range. The equivalence between the limited dynamic range of optical spectral analysis and phase wrapping in MPF systems is analytically and experimentally validated. Furthermore, we explore the feasibility of machine learning (ML) algorithms to optimize magnitude–phase analysis, enabling seamless integration while maintaining accuracy over an extended dynamic range. Experimental results confirm the effectiveness of the proposed method, paving the way for next-generation intelligent fiber optic sensing systems with enhanced adaptability and precision.

Department(s)

Electrical and Computer Engineering

Publication Status

Early Access

Keywords and Phrases

Dispersion Fourier transform (DFT); fiber optic sensor; interferometry; microwave photonics (MWP)

International Standard Serial Number (ISSN)

1557-9670; 0018-9480

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2025 Institute of Electrical and Electronics Engineers, All rights reserved.

Publication Date

01 Jan 2025

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