相位敏感光时域反射(Φ-OTDR)传感系统具有高动态响应、高灵敏等特点，在大型工程结构健康监测领域具有巨大的应用潜力。而Φ-OTDR系统仪器化水平和工程应用很大程度上取决于数字信号处理(DSP)技术。本文对比分析了近年来Φ-OTDR系统在信号的量化、解调、抑噪以及模式识别上主要的数字信号处理方法和技术，并通过架空输电线路状态监测、埋地电缆外破预警两个应用实例，阐述了工程应用中数字信号处理与行业背景知识相结合的重要性和方法，并对Φ-OTDR系统中数字信号处理方法的发展现状和趋势进行了总结与展望。

Overview: The phase-sensitive optical time-domain reflectometry (Φ-OTDR) sensing system has the characteristics of high dynamic response and high sensitivity, and has great application potential in the field of large-scale engineering structural health monitoring. The instrumentation level and engineering application of Φ-OTDR systems depend to a large extent on digital signal processing (DSP) technology. For the Φ-OTDR system, the tasks of digital signal processing mainly include three aspects. First, the demodulation of Rayleigh's backscattered light phase information should be completed accurately and efficiently. It is necessary to understand the relationship between the phase difference and the sound field signal. Then, it is necessary to reasonably set the core parameters of the Φ-OTDR system in the digital-to-analog conversion to obtain the RBS signal quickly and accurately. After that, it is necessary to select an appropriate demodulation method for demodulation. Second, all kinds of noise floor of the sensing system itself should be analyzed and suppressed. Since the noise floor of the sensing system itself is inevitable, analyzing and suppressing it is the key to improve the signal-to-noise ratio of the system. The drift of the laser center frequency, the local birefringence change of the fiber, and the nonlinear correspondence between the fiber strain and the interference intensity will all introduce corresponding noise to the system. Among the many types of noise, the coherent fading brought by the system will cause the system SNR to continue to deteriorate and randomly form detection blind spots; the polarization-related noise caused by the external environment will affect the Φ-OTDR system's ability to perceive multiple disturbance events. Third, reliable feature extraction and pattern recognition strategies should be quickly selected to improve the accuracy and intelligence of system reconstruction disturbance events. In engineering applications, various monitoring objects and time-varying background noise make it difficult to describe vibration events by accurate mathematical models. In particular, when Φ-OTDR is used in new scenarios, it needs to be able to quickly establish a corresponding analysis model based on industry knowledge, and minimize the degree of manual participation in it. Therefore, efficient and reliable object feature extraction methods, pattern recognition algorithms, and machine learning strategies are urgently needed. In view of the above problems, this paper summarizes the main digital signal processing methods and technologies of the Φ-OTDR system in recent years in the digitization of optoelectronic signals, the demodulation of phase information, the suppression of system noise, and the pattern recognition of detected objects. Two application cases of transmission line condition monitoring and buried cable breakage early warning illustrate the digital signal processing skills in the design of engineering application schemes.The phase-sensitive optical time-domain reflectometry (Φ-OTDR) sensing system has the characteristics of high dynamic response and high sensitivity, and has great potential in the field of large-scale engineering structural health monitoring. The instrumentation level and engineering application of Φ-OTDR systems depend to a large extent on digital signal processing (DSP) technology. This paper compares and analyzes the main digital signal processing methods and technologies of Φ-OTDR systems in signal quantization, demodulation, noise suppression, and pattern recognition in recent years. The importance and method of combining digital signal processing with industry background knowledge in engineering applications are expounded, and the development status and trend of the digital signal processing methods in Φ-OTDR systems are summarized and prospected.