Fiber-optic gyroscopes (FOGs) have become one of the most important elements of modern inertial navigation systems due to their high accuracy, reliability, and independence from external signals such as satellite navigation. This review analyzes and discusses the key FOG architectures: interferometric (IFOG), resonant (RFOG), digital (DFOG), and hybrid (HFOG). The concepts of their functioning, structural features, and the main advantages and limitations of each architecture are examined. Particular focus is placed on advanced signal-processing and error-compensation algorithms, including filtering techniques, noise suppression, mitigation of thermal and mechanical drifts, and emerging machine learning (ML) based approaches. The analysis of these architectures is carried out in terms of major parameters that determine accuracy, robustness, and miniaturization potential. Various applications of FOGs in space systems, ground platforms, marine and underwater navigation, aviation, and scientific research are also being considered. Finally, the latest development trends are summarized, with a particular focus on miniaturization, integration with additional sensors, and the introduction of digital and AI-driven solutions, aimed at achieving higher accuracy, long-term stability, and resilience to real-world disturbances.

This article presents a detailed analytical review of distributed acoustic sensing (DAS) systems for seismic monitoring, with emphasis on their optical infrastructure, signal processing methodologies, and integration with machine learning (ML) approaches. DAS leverages existing fiber-optic cables by utilizing Rayleigh backscattering to convert them into high-resolution seismic sensing networks. These networks offer spatial resolution down to one meter and can operate effectively over distances up to 250 kilometers in real time. The system’s responsiveness to pressure and environmental fluctuations has been captured through mathematical modeling. To enhance signal quality, seismic data is subjected to advanced processing techniques, including Fourier analysis, wavelet transformation, and adaptive noise filtering, yielding signal-to-noise improvements of up to 15 dB. In terms of data interpretation, machine learning models such as support vector machines (SVM), long short-term memory networks (LSTM), and gradient boosting classifiers have achieved high performance, often surpassing 90% accuracy in seismic event detection. Furthermore, scalable insights are supported through unsupervised and semi-supervised learning strategies. To address challenges related to model transparency, explainability tools like SHAP and LIME are applied to aid in the interpretation of predictive outputs. Field deployments of DAS systems, combined with intelligent analytics, demonstrate significant promise for large-scale seismic detection across both terrestrial and marine environments.

The increasing complexity and extended operational lifetimes of modern space infrastructure have significantly intensified the demand for reliable structural health monitoring (SHM) systems. However, the extreme space environment, characterized by radiation exposure, microgravity, ultra-high vacuum, and severe thermal cycling, imposes critical limitations on conventional electrical sensing technologies, leading to reduced measurement accuracy, instability, and long-term degradation. This review presents a comprehensive analysis of fiber Bragg grating (FBG)-based sensing technologies as a promising solution for deformation monitoring in space infrastructure. The study investigates the fundamental operating principles of FBG sensors under space conditions and systematically classifies existing FBG-based SHM architectures, including point-based, multiplexed, long-distance, and hybrid sensing systems. Furthermore, the advantages of FBG sensors—such as immunity to electromagnetic interference, passive operation, and high-resolution multipoint sensing—are critically evaluated in comparison with traditional electrical sensors. In addition, key challenges affecting the performance of FBG systems in space environments are analyzed, including radiation-induced wavelength drift, temperature–strain cross-sensitivity, signal attenuation, and long-term stability issues. The paper also highlights recent advances in interrogation techniques and network architectures that enable reliable in situ and real-time deformation monitoring of space structures. The results demonstrate that FBG-based sensing systems provide a scalable and robust framework for SHM in extreme environments while also revealing existing limitations and open research challenges. This work establishes a structured foundation for the development of next-generation intelligent monitoring systems for space infrastructure.

The object of this study is the deformation process in reinforced-concrete structural elements equipped with embedded fiber-optic sensors. The problem addressed corresponds to unresolved issues identified in previous studies – namely, the lack of standardized quantitative evaluation of accuracy and stability in fiber-optic deformation measurement. Despite high laboratory precision, existing methods show reduced long-term reliability, temperature-strain cross-sensitivity, and calibration inconsistency when applied to real structures. The main results show that fiber Bragg grating (FBG) and interferometric sensors achieved sub-micrometer deformation resolution with deviations below 2–3 με and long-term drift under 0.5%. Measurements remained stable under variable loading and temperature, confirming high reproducibility and electromagnetic immunity. These findings validate the hypothesis that optical wavelength shifts directly correspond to mechanical strain, ensuring reliable strain detection without recalibration. This effectiveness stems from the intrinsic photoelastic coupling and refractive-index sensitivity of the optical fiber, which provide nanometric resolution, corrosion resistance, and long-term operational stability. The proposed method is applicable for long-term monitoring of bridges, tunnels, and high-rise facilities exposed to environmental and cyclic stresses. Therefore, research on high-precision fiber-optic deformation measurement remains scientifically relevant for improving the safety and durability of modern civil engineering structures.

This paper provides a comprehensive overview of the progress in fiber-optic gyroscope technology, covering 260 key studies of the last ten years. A critical comparative analysis of fiber-optic gyroscope with alternative inertial sensors (Micro-Electro-Mechanical Systems, Hemispherical Resonator Gyroscope, Ring Laser Gyroscope) has been carried out. Confirming the unique advantages of fiber-optic gyroscope for autonomous navigation. Fundamental limitations of accuracy are considered in detail: temperature drifts, polarization noise, and Rayleigh backscattering. Modern hardware methods for suppressing these errors, including the use of photonic crystal and hollow fibers (Air-Core/Hollow-Core), are also considered in this work. The central place in the review is occupied by the analysis of the technological paradigm shift from bulky discrete circuits to hybrid integrated photonics (Indium Phosphide, Silicon Nitride, Lithium Niobate) and hybrid architectures to reduce weight and size characteristics. The role of artificial intelligence (Deep Learning, Long Short-Term Memory) methods in nonlinear drift compensation and calibration is discussed. The usage of the Brillouin effect and optomechanics promising areas are outlined, necessary to create a new generation of navigation systems operating in the absence of Global Navigation Satellite Systems signals.

Spacecraft and launch-vehicle structures require lightweight multipoint monitoring under combined mechanical, thermal, and environmental loads. This study presents an analytical fiber Bragg grating (FBG) sensor-network workflow integrating reference-grating temperature compensation, regional strain assessment, opposite-surface curvature sensing, wavelength-division-multiplexing allocation, and strain-to-shape reconstruction. The deterministic compensation case is used only as a self-consistency check, whereas practical robustness is assessed through 10,000 Monte Carlo trials incorporating packaged-coefficient mismatch, temperature nonuniformity, wavelength noise, strain-transfer variation, drift, and calibration uncertainty. The calibrated estimator achieved a median strain mean absolute error of 1.73 με and a 95th-percentile error of 4.22 με. The defined finite-element benchmarks produced a maximum engine-mount truss strain of 456.2 με under the defined loads and a median full-field panel-reconstruction normalized root-mean-square error of 1.29% for 18 sensing locations with 2 με noise. Conservative WDM analysis yielded 54, 13, and 16 channels for three operating envelopes, and the prescribed random-vibration spectrum produced 6.78 g_rms. These results demonstrate a reproducible numerical proof of concept and define practical limits for compensation, spectral allocation, curvature interpretation, and inverse reconstruction; they do not constitute experimental validation or flight qualification.