Yiyang Zhuang’s research while affiliated with Zhejiang Mariculture Research Institute and other places

The phase-sensitive optical time domain reflectometry scheme has attracted great research interest in distributed acoustic sensing (DAS) but suffers from a trade-off between the dynamic range and signal frequency. In this paper, an optical path difference (OPD) demodulation method is applied to an interferometer array composed of an ultra-weak fiber Bragg grating (UWFBG) array and an imbalanced Michelson interferometer to solve this problem. As far as we know, this is the first time that OPD demodulation has been applied to DAS. The UWFBG array is interrogated by a frequency-modulated pulse, and the acoustic signal sensed by the fiber between any two adjacent UWFBGs can be retrieved by demodulating the variation of residual OPD of the interferometer formed by them. The proposed method is analyzed theoretically and validated experimentally; compared with the phase demodulation method, a 100-times boost in bandwidth is achieved for a signal with an amplitude of 0.4 µε. Results also show that the proposed method offers increasing signal-to-noise ratios as the frequency increases.

The measurement of acoustic signals at high frequencies with a large dynamic range remains a challenge for phase-demodulation-based fiber acoustic sensing systems due to the phase wrapping phenomenon. Here, an optical path difference (OPD)-assisted demodulation method aiming at breaking the limitations of phase demodulation is reported. By interrogating an imbalanced Michelson interferometer (IMI) with a frequency-modulated pulse light, a time-resolved interference spectrum is obtained and used for OPD demodulation. Theoretical analysis reveals that the variation amplitude of demodulated OPD signal not only is linearly dependent on the amplitude of the acoustic signal applied to the IMI, but also experiences a gain that is directly proportional to the signal frequency. Consequently, compensation of this gain introduces a noise suppression that favors high-frequency signals, resulting in a higher signal-to-noise ratio (SNR) at high frequencies. The method's capability for detecting both single-frequency and multi-frequency acoustic signals is demonstrated by simulations and experiments, and the results show that the upper-frequency limit of the proposed scheme is at least 40 times the limit of the phase demodulation method for an acoustic signal with an amplitude of 1.135 μϵ. The proposed method can be easily extended to distributed acoustic sensing systems and is of great potential to break the limitation of a restricted dynamic range encountered in traditional phase demodulation methods.

The signal-to-noise ratio (SNR) of fiber-optic distributed acoustic sensing (DAS) systems based on Rayleigh backscattered (RBS) signals can be improved using point-backscattering-enhanced fiber (PBSEF). It is necessary to explore the methods to achieve stable and low noise demodulation for PBSEF-based DAS system. Driven by this, this work theoretically analyzes the effect of the phase and intensity noises on the PBSEF-based demodulated signal during averaging process. The results show that the phase noise has a weaker impact on the demodulated phase than the intensity noise. Based on this analysis, two methods, direct averaging and weighted averaging, for suppressing the noise level of DAS have been investigated and the optimal parameters are investigated. For both methods, simulation and experiments show the noise level suppression of 1.5 and 4.4 times, corresponding to the noise level reduction of 0.45 and 3.78 $\text{p}\varepsilon /\surd$ Hz, respectively. The relationship between the system noise level and the number of sampling points used in the demodulation is different for the two methods. The weighted-averaging method demonstrates superior performance for its higher stability. These conclusions provide significant theoretical basis and guidance for the development of practical high-performance DAS systems for field applications.

An OPD-based demodulation method for acoustic signal detection is proposed, breaking the limit of phase demodulation. Experiment results demonstrated superior performance at high frequency, and the SNR increases proportionally with the frequency of the signal.

A stable and low noise demodulation for PBSEF based DAS system is explored by theoretical and experimental analysis. The results show that, direct-average and weighted-average, for suppressing the noise level of DAS and the optimal parameters are investigated.

Distributed acoustic sensing (DAS) system with point-backscattering-enhanced fiber (PBSEF) has the capability of breaking the signal to noise ratio limit of traditional DAS, therefore, it would become one of the most powerful DAS technologies and expand the applications of DAS systems. To optimize the performance when designing DAS system with PBSEF, it is crucial to understand the relationship between the system noise and the reflectance enhancement, which is yet to be systematically studied. In this paper, both experimental investigation and theoretical analysis of a typical 3×3 heterodyne DAS system with PBSEF were carried out. The relationship between system noise level and the reflectance enhancement was studied by both experiments and simulations. And the results, with good agreements between each other, show that the system noise level drops exponentially as the reflectance increases. With 29.7 dB reflectance enhancement, the system noise level decreased from 9.5 pε/√Hz to 0.44 pε/√Hz. The influences of intensity noise and phase noise were also analyzed based on the theoretical model. The findings of the quantitative relationship and the noise influence can be used as a universal guidance for the design of PBSEF-based high performance DAS systems.

A novel method is proposed to correct the distortions induced by phase unwrapping error. The method is further validated in processing the data acquired from a field test performed in ocean environments using a DAS system.

In this article we report a microwave photonics fiber loop ringdown (MWP-FLRD) interrogated fiber-optic sensor with enhanced sensitivity. A section of a single-mode fiber in the fiber loop is macro-bent into a circular ring as the element responsive to displacement. When the circular ring is subjected to external displacement, the transmission loss of the loop changes, resulting in a change in the ringdown time of the MWP-FLRD system. To enhance the measurement sensitivity, a multi-turn structure is proposed and experimentally demonstrated. Compared to the traditional time-domain FLRD system, the MWP-FLRD system employs a frequency domain measurement, showing a high signal-to-noise ratio and high measurement efficiency. The physics of MWP-FLRD, the displacement measurement principle, the sensitivity-enhancement scheme, and the temperature self-compensation capability of the system are discussed in this paper. The experimental results demonstrate that an averaged amplification factor of 0.8221 per turn is achieved for displacement measurement sensitivity using the multi-turn structure. Taking advantage of the mechanical strength of the multi-turn fiber structure with an intact polymer coating, the system is employed to monitor the elbow bending of human body movement in real-time, showing high sensitivity and fast responses. It is envisioned that the concept of the proposed multi-turn structure can be implemented onto other FLRD systems for sensitivity enhancement.

Simultaneously increasing the sensitivity and dynamic range of an optical fiber sensor is desired and yet challenging. In this article, we demonstrate an optical fiber curvature sensor based on a no-core fiber (NCF) cascaded with a hollow-core fiber (HCF), realizing simultaneously high sensitivity and a large dynamic range with the assistance of machine learning analysis. The sensor is fabricated by simply fusion splicing a section of NCF and HCF to two single-mode fibers (SMFs), forming the SMF-NCF-HCF-SMF hybrid structure. It is shown that the multimode interference in the NCF can increase the sensitivity of the device for curvature measurements, compared to the conventional SMF-HCF-SMF structure. However, the enhanced sensitivity poses a limitation on the dynamic range of the proposed curvature sensor. We propose the use of machine learning to analyze the measured spectra of the sensor device, achieving one-to-one mapping between the measured raw spectrum and the exerted curvature on the sensor, and thereby the issue of the limited dynamic range is resolved. The proposed strategy, enabling the co-existence of high sensitivity and a large dynamic range, is a highly generalizable technique and can be extended to other optical fiber sensors for measuring other physical, chemical, and biological quantities in different applications.