Jian Li

Raman optical time-domain reflectometry (ROTDR) inherently balances sensing range, spatial resolution, and temperature accuracy through the pulse duration dictated by the OTDR position principle. However, optimizing one metric conventionally degrades the others, forming a theoretical trade-off. This work introduces complex-domain square-wave width-chirp pulse compression to break that physical limitation. The steep edges and rich high-order harmonics of complex-domain square-wave width-chirp pulse undergo matched filtering, producing a compressed δ-pulse whose full width at half maximum, rather than the original pulse duration, now governs sensing spatial resolution. Complex-domain matched filtering, implemented via a conjugate time-reversal filter, achieves a 15.09 dB gain in signal-to-noise ratio, while the complex-domain envelope extraction method isolates and removes Raman phase noise. The proposed scheme simultaneously achieves 45 km sensing distance, 0.5 m spatial resolution, and 0.11 °C temperature accuracy, demonstrating complete decoupling of these metrics from the pulse duration. The proposed framework offers a new paradigm for long-range, high-precision distributed temperature sensing and is extensible to Brillouin and Rayleigh scattering systems.

针对传统拉曼分布式光纤传感技术中由于米量级空间分辨率性能不足，导致传感光纤沿线亚空间分辨率长度探测光纤区域内系统测量精度下降的技术瓶颈，本研究提出一种基于一维寻峰方法的阈值系数拟合方法。该方法通过提取探测光纤区域内分布式温升曲线的峰值系数和半高全宽（full width at half maximum,FWHM） ，然后建立拉曼散射阈值系数拟合模型及“FWHM-传感距离-探测光纤尺寸”的定量映射模型，进而计算出最优拉曼散射阈值系数，最终重构光纤沿线分布式温度场信号。实验结果表明：应用该技术后，在10 km传感距离下，系统在30 cm探测光纤上的测温误差相较于传统温度解调技术（34.7℃）显著降低，温度精度达到1.5℃。此外，FWHM与传感距离呈线性正相关，且独立于温度变化，该特性确保了该技术在不同环境温度下的稳定性和适应性。本论文通过纯算法方案重构光纤沿线拉曼散射信号，无须增加额外器件，为高精度分布式温度监测在长距离基础设施健康诊断等领域的应用提供了一种新方案。

The practical implementation of Raman distributed optical fibre sensing has been fundamentally constrained by the inherent low signal-to-noise ratio (SNR), particularly for sensing distance exceeding 30 km. We propose a paradigm that combines enhanced anti-distortion coding processing, advanced Raman scattering waveform reconstruction preprocessing, and Haar wavelet denoising to transcend this physical limitation. The proposed pre-processing framework simultaneously optimises complementary sequences correlation, effectively mitigates disturbances of transient effects and improves sensing performance. The experimental demonstration achieves performance metrics: 70.0 km sensing distance with 1.58 m spatial resolution, while maintaining 0.91 °C measurement accuracy and 5.39 °C temperature resolution. The tripartite synergistic mechanism, consisting of the waveform reconstruction pre-processing framework compensating for and suppressing transient effects, coding gain improving the baseline SNR, and the Haar wavelet transform removing residual noise, breaks through the theoretical trade-off constraint between SNR and sensing distance in traditional schemes. The proposed approach demonstrates a potential for application in the fields of long-range infrastructure monitoring and environmental sensing.

The practical implementation of Raman-based distributed optical fiber sensing has been fundamentally constrained by the inherent low signal-to-noise ratio, particularly for operational ranges exceeding 30 km. We present a groundbreaking paradigm integrating Golay-encoded autocorrelation processing with advanced Raman scattering waveform reconstruction to transcend this physical limitation. A newly developed preprocessing framework simultaneously optimizes complementary sequence correlation and effectively mitigates disturbances of transient effects. Our experimental demonstration achieves record-breaking performance metrics: 70 km operational range with 1.58 m spatial resolution, while maintaining 0.88 °C measurement accuracy and 5.44 °C temperature resolution. This methodology establishes new technical benchmarks by successfully resolving the persistent trade-off between sensing distance and spatial resolution in distributed fiber-optic sensing systems, opening new possibilities for long-range infrastructure monitoring and environmental sensing applications.

A high-accuracy distributed temperature sensing scheme utilizing chaotic Brillouin scattering power combined with the cross-correlation measurement method was proposed and experimentally demonstrated. The proposed scheme employs the chaotic laser as the sensing signal, adopting the chaotic cross-correlation positioning principle instead of the conventional optical time-domain reflection principle. Additionally, it combines a single-path ratio method to accurately demodulate the temperature. This positioning method uses the random amplitude characteristics of chaotic lasers in the time domain. It overcomes the limitation of pulse width on spatial resolution in distributed temperature sensing based on OTDR principles. And it resolves the trade-off between spatial resolution and sensing distance. Experimental results demonstrated that the spatial positioning accuracy was optimized from 5.0 to 0.43 m with a 50 ns pulse width modulation, achieving a sensing distance of up to 10 km. This scheme aims to achieve high spatial positioning accuracy while maintaining a long sensing distance. This is crucial for early fire warning and effective emergency response in long-distance fire monitoring applications.

Raman distributed temperature sensors enables distributed measurement of temperature along the sensing fiber, which is widely used in production. Due to the limitation of the optical time-domain reflection principle, the temperature measurement accuracy of the sensors will be seriously affected when the length of the temperature variation region is shorter than the pulse width. In this work, the relationship between pulse width, fiber under test (FUT) length and FUT temperature is firstly studied. Then, a multi-pulse parallel demodulation scheme is proposed. The experiments depict that a temperature measurement accuracy is optimized from over 30°C to 1.55 °C with a 0.2 m temperature variation region over 5.6 km. The scheme is based on multi-pulse parallel measurements combined with numerical simulations to achieve equivalent short pulse measurements of the small-region temperature variation without changing the hardware structure of the sensors. This scheme reduces the hardware requirements of the sensors and avoids the problems of sensing distance reduction and signal-to-noise ratio deterioration caused by shortening the pulse width.

The existing kilometer-level Raman-distributed optical fiber sensing mechanism encounters a physical bottleneck as its spatial resolution is limited to several meters, due to the pulse width of more than ten nanoseconds in the principle of optical time-domain reflection (OTDR). In this work, we propose a differential pulse-width pair detection scheme (DPPDS) for Raman-distributed optical fiber sensing. First, we examined the transmission and superposition effect of pulse-pair detection in the sensing fiber and developed the theoretical model. It proved that the differential pulse-width pair sensing demodulation effectively extracted small temperature signals from the ambient temperature. Further, the experiment results depict the proposed scheme can achieve a 0.38 m spatial resolution over 6.0 km. As far as we know, this is the highest sensing spatial resolution achieved by kilometer-level Raman-distributed optical fiber sensing. This study provides a new research idea to improve the sensing spatial resolution of most distributed optical fiber sensing schemes based on the OTDR positioning principle.

Raman distributed optical fiber sensing has the unique ability to measure the spatially distributed profile of temperature that are of great interest to numerous field applications. However, the sensing performance is severely limited by the signal-to-noise ratio (SNR). The existing SNR enhancement schemes have drawbacks such as increased system complexity, degradation of sensor performance metrics such as spatial resolution, poor denoising performance, etc. Here, we report the Raman residual composite dual-convolutional neural network (RRCDNet), a novel convolutional neural network-based denoising model for one-dimensional signals specifically tailored to Raman distributed fiber sensing. The RRCDNet-enhanced Raman distributed fiber sensor system dramatically improves the temperature precision by more than a factor of 100, from 7.57 °C to 0.06 °C, without hardware modification or degradation of other performance metrics. At the same time, RRCDNet can also enhance other optical fiber sensor systems with one-dimensional signals, such as Rayleigh and Brillouin sensing systems.

The physics principle of pulse flight positioning is the main theoretical bottleneck that restricts the spatial resolution of the existing Raman distributed optical fiber sensing scheme. Owing to the pulse width of tens of nanoseconds, the spatial resolution of the existing Raman distributed optical fiber sensing scheme with kilometer-level sensing distance is limited to the meter level, which seriously restricts the development of the optical time-domain reflection system. In this paper, a chaos laser is proposed in the context of the physical principle of the Raman scattering effect, and a novel theory of chaos Raman distributed optical fiber sensing scheme is presented. The scheme reveals the characteristics of chaos Raman scattering light excited by a chaotic signal on the sensing fiber. Further, the chaos time-domain compression demodulation mechanism between the temperature variation information and chaos correlation peak is demonstrated. Then, the position of the temperature variation signal is precisely located using the delay time of the chaos correlation peak combined with the chaos pulse flight time. Based on this novel optical sensing mechanism, an experiment with 10 cm spatial resolution and 1.4 km sensing distance was conducted, and the spatial resolution was found to be independent of the sensing distance. Within the limit of the existing spatial resolution theory, the spatial resolution of the proposed scheme is 50 times higher than that of the traditional scheme. The scheme also provides a new research direction for optical chaos and optical fiber sensing.