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2 2025

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Group alternating integration for distributed acoustic sensing with improved signal-to-noise ratio

Angelsky Oleg, Strynadko Myroslav, Zenkova Claudia, Zheng Jun

Opto-Electronics Review

2025

Vol. 33, No. 4

art. no. e155878

Distributed acoustic sensing (DAS) based on Rayleigh backscattering is actively used for perimeter monitoring of critical infrastructure. However, traditional signal processing methods often face challenges in detecting weak or short-term events in noisy conditions. In this paper, we propose an improved signal accumulation method based on group averaging with alternating sign integration. The proposed method provides the efficient noise suppression and improves the detection of local mechanical disturbances along the fibre. Comparative simulation study between the classical and the proposed approach demonstrates a significant improvement in signal visibility in the presence of additive noise. Potential implementations of multi-layered and mesh-integrated DAS configurations are also discussed to further enhance signal-to-noise ratio (SNR). The obtained results can serve as a basis for the development of modern security systems for critical facilities.

Automated polarisation-optical method for diagnostics of railway rails surface defects

Angelsky Oleg, Strynadko Myroslav, Zenkova Claudia, Zheng Jun

Opto-Electronics Review

2025

Vol. 33, No. 3

art. no. e155876

This paper presents a novel polarisation-optical method for the automated inspection of surface defects in railway rails. The proposed approach is based on the detection of changes in the polarisation state of reflected laser radiation, caused by local anomalies such as cracks, residual stress zones, and surface contamination. A differential signal is generated by separating orthogonal polarisation components using a polarising beam splitter, enabling high sensitivity to surface irregularities while suppressing common-mode noise. To improve the interpretability of the acquired signal, a multi-stage digital processing pipeline is employed. It includes moving average and Gaussian filtering, threshold-based segmentation, and frequency-domain analysis using both discrete Fourier and continuous wavelet transforms. The method was validated through a set of simulated signals, imitating typical rail defects in noisy conditions. The results demonstrate reliable detection and localisation of structural anomalies, with a clear distinction between sharp discontinuities and wide modulations caused by tension. Due to its compatibility with automated processing frameworks, the method is well suited for integration with modern diagnostic platforms, including mobile rail inspection systems. This makes it a promising candidate for predictive maintenance strategies and digital transformation of railway infrastructure monitoring.