This paper presents a vehicle detection and classification system using distributed fiber-optic acoustic sensing (DAS) technology and describes a comprehensive classification method including signal processing and feature extraction. This sensing device is based on Rayleigh scattering light and is used for real-time vehicle detection, classification, and speed estimation. Distributed acoustic signals from an arbitrary point can be detected and located through DAS technology which can provide fully distributed acoustic information along the entire fiber link. This technology utilizes sensing fiber in the form of distributed sensors to collect traffic vibration signals and then extracts several features from the signals to estimate the vehicle count and identify vehicle categories. According to the vehicle vibration signal characteristics, the wavelet-denoising algorithm and dual-threshold algorithm are improved. The improved algorithm is used to reconstruct the signal for feature extraction, and the vehicle count and speed are obtained. When all features have been extracted, the classification of vehicle types is implemented by a support vector machine classifier. The validation data (using a distributed fiber-optic acoustic sensor) demonstrate that the vehicle detection accuracy is higher than 80%, the speed estimation error is less than 5%, and the vehicle classification accuracy is higher than 70%.

Distributed Acoustic Sensing (DAS) is a novel technology that uses fiber optics to sense and monitor vibrations. It has demonstrated immense potential for various applications, including seismology research, traffic vibration detection, structural health inspection, and lifeline engineering. DAS technology transforms long sections of fiber optic cables into a high-density array of vibration sensors, providing exceptional spatial and temporal resolution for real-time monitoring of vibrations. Obtaining high-quality vibration data using DAS requires a robust coupling between the fiber optic cable and the ground layer. The study utilized the DAS system to detect vibration signals generated by vehicles operating on the campus road of Beijing Jiaotong University. Three distinct deployment methods were employed: the uncoupled fiber on the road, the underground communication fiber optic cable ducts, and the cement-bonded fixed fiber optic cable on the road shoulder, and compared for their outcomes. Vehicle vibration signals under the three deployment methods were analyzed using an improved wavelet threshold algorithm, which was verified to be effective. The results indicate that for practical applications, the most effective deployment method is the cement-bonded fixed fiber optic cable on the road shoulder, followed by the uncoupled fiber on the road, and the underground communication fiber optic cable ducts are the least effective. This has important implications for the future development of DAS as a tool for various fields.

An improved phase-sensitive optical time-domain reflectometry (φ-OTDR) system with sub-meter spatial resolution is demonstrated. Two Michelson interferometers (MIs) with different path length differences are used in the proposed system. One is 10 m, the other is 9.2 m. Two Rayleigh backscattering phase traces with different spatial resolution are obtained by a phase generated carrier (PGC) algorithm at adjacent times. After using differencing and adaptive 2-D bilateral filtering algorithms, a 0.8-m spatial resolution over 2 km is achieved. Experimental results indicate that the system shows an extraordinary linearity as high as 99.94% with amplitude-modulation and acquires a detection frequency from 5 to 500 Hz.

We propose a seawater salinity sensor array based on a micro/nanofiber Bragg grating structures, which allows for the simultaneous measurement of temperature and salinity. The proposed sensing structure is fabricated through a process involving optical fiber tapering, femtosecond laser inscription, and chemical etching. The equivalent refractive index (RI) of this sensor structure is influenced by the surrounding RI, resulting in a Bragg characteristic wavelength shift that can be used for salinity sensing. The experimental results show that the salinity sensitivity for two cascaded sensor arrays is 8.39 pm/‰ and 7.71 pm/‰, while the temperature sensitivity is 8.28 pm/°C and 8.03 pm/°C, respectively. This sensor structure is compact, exhibits excellent linearity, and offers good repeatability. It holds great potential for applications in seawater environmental monitoring and quantitative studies of seawater dispersion characteristics.

Optical pulse coding phase-sensitive optical time-domain reflectometry ( $\Phi$ -OTDR) sends a series of pulses into the sensing fiber to enhance signal-to-noise ratio (SNR). And the spatial resolution is determined by the subpulse duration. The current challenges in optical pulse coding $\Phi$ -OTDR include long measurement time, complex coding design, and intricate modulation process. In this paper, pulse intensity coding and mismatched filtering is introduced, which could reach the theoretical measurement time determined by the fiber length. Additionally, it also simplifies coding design and modulation process. In order to obtain satisfactory decoding results, the least-squares criterion is used to design the mismatched filter. Experimental result demonstrates a 6.6 dB improvement in the SNR of the demodulated signal using a 10-bit coding pulse.

Pulse compression enhances the signal-to-noise ratio (SNR) in distributed fiber optic acoustic sensing (DAS) by increasing pulse energy through cross-correlation, while maintaining spatial resolution. In DAS systems, linear frequency modulation (LFM) pulses are commonly used; however, their limited sidelobe suppression (SLR) results in increased noise, limiting improvements in SNR and fading noise mitigation. To overcome these limitations, we propose an adaptable NLFM pulse design methodology that optimizes SLR based on specific application requirements. This approach significantly enhances pulse energy injection while reducing system noise, thereby improving overall sensing performance. Additionally, dual-carrier frequency-division multiplexing is employed to maximize energy utilization and mitigate fading effects. The experimental results demonstrate that, compared to the LFM-based detection pulse system, the optimized NLFM pulse improves the SNR by 10 dB. Under identical conditions, the NLFM system also enhances its performance in suppressing fading noise. Furthermore, the use of dual carriers effectively reduces the hardware resource consumption of the sensing system, highlighting the great potential of NLFM pulses in the field of fiber optic sensing.