We propose a broadband chirped-pulse conversion algorithm (BCPCA) to convert a finite-step scanning probe pulse into an equivalent broadband chirped probe pulse by convolving a chirp factor on the received signal in coherent phase-sensitive optical time domain reflectometry (Φ-OTDR). Combined with Rayleigh interference pattern (RIP) demodulation in chirped-pulse Φ-OTDR (CP-ΦOTDR), environmental perturbations, such as strain and temperature along the sensing fiber, can be quantitatively measured. The equivalent broadband chirped pulse is generated by digital processing, and its bandwidth can be increased by changing the composition of the scanning pulse. Thus, the measurable perturbation range of the system can be expanded. As a proof-of-concept experiment, a high-performance distributed strain measurement was realized on a 10 km fiber, the frequency response was 5 kHz, which is only limited by the fiber length, and the strain resolution was 8.04 p ε / H z . The proposed method of generating equivalent broadband chirped pulse through the digital domain can be used as a supplement to CP-ΦOTDR.

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.

The key point on analyzing the data stream measured by fiber optic distributed acoustic sensing (DAS) is signal activity detection separating measured signals from environmental noise. The inability to calculate the threshold for signal activity detection accurately and efficiently without affecting the measured signals is a bottleneck problem for current methods. In this article, a novel signal activity detection method with the adaptive-calculated threshold is proposed to solve the problem. With the analysis of the time-varying random noise’s statistical commonality and the short-term energy (STE) of real-time data stream, the top range of the total STE distribution of the noise is found accurately for real-time data stream’s ascending STE, thus the adaptive dividing level of signals and noise is obtained as the threshold. Experiments are implemented with simulated database and urban field database with complex noise. The average detection accuracies of the two databases are 97.34% and 90.94% only consuming 0.0057 s for a data stream of 10 s, which demonstrates the proposed method is accurate and high efficiency for signal activity detection.