## 1 Introduction  
Phase-sensitive optical time-domain reflectometry (\(\Phi\)-OTDR), a kind of optical time-domain reflection technology, can respond to vibrations applied to any point along a fiber and demodulate the key information of the target events through the backscattered light. Given the characteristics of long-distance, high-sensitivity, high-stability, and full-scale monitoring of \(\Phi\)-OTDR, it is widely used in fields like perimeter security, pipeline monitoring, seismic monitoring, and structural health monitoring [1, 2]. As both a transmission medium and sensor, the application of optical fibers will develop more rapidly in the era of 5G and big data. In the field of intrusion monitoring, lots of research has been conducted in the laboratory or other ideal environments. However, in practical environments,the monitoring signals are nonlinear, nonstationary, and easily interfered with by strong noise [3]. Furthermore, the fading effect makes it more difficult to denoise and recognize the signal. The fact that the phase measurement noise is different across the different trace positions implies that the measurements present a spatially varying sensitivity along the fiber. These problems still hinder the development of \(\Phi\)-OTDR for practical applications. The fading effect is a phenomenon that takes place during detection using a \(\Phi\)-OTDR system, where the scattered light falls into the dead area of the measurements and the light intensity makes it difficult to allow for signal analysis. The fading effect makes it difficult to allow for the effective detection of the signal at some points and obtain the waveform characteristics of the time-domain detection. Therefore, it is impossible to locate and identify the event in time. So far, a large amount of work has focused on this problem. [PERSON] proposed a method based on time-gated digital optical frequency domain reflectometry and a novel vibration-locating method using statistical analyses of phase-change signals [4; 5; 6]. There have also been some studies that used frequency division multiplexing and other methods to suppress the fading effect [7; 8; 9; 10; 11]. Due to the high sensitivity of the \(\Phi\)-OTDR system, the detected signal is mixed with different kinds of noises. The intensity of the noise signal is often similar to, or even larger than, the target signal. It causes the target signal to be submerged, meaning it is difficult to effectively extract and represent valid information in the signal. Furthermore, the monitoring data can generate more than 2 GB of storage files per second at a high sampling frequency, which makes most of the methods difficult to use in real time. Considerable research efforts have been devoted to avoiding these problems, but this is typically at the expense of increasing the system computational load or sacrificing the sensing performance [12; 13]. [PERSON] used the fitting method to eliminate the noise's impact [14]. A novel location scheme proposed by [PERSON] et al. requires a lot of calculations when presented with large amounts of detection data [15]. [PERSON] proposed a method based on the ultra-weak Bragg grating, which increased the hardware burden [16]. The method based on deep learning models requires very demanding computing conditions and very high computing times [17; 18], which like the black box, is not conducive for researchers to analyze the nature of the \(\Phi\)-OTDR issues. Despite this, these studies have either increased the number of computations by switching calculations to the frequency domain or by using statistical calculations, or have increased the cost of the hardware to achieve the aim of suppressing the fading effect and improving the performance of \(\Phi\)-OTDR. The above studies have not specifically studied the enhancement of signal characteristics and the improvement of computing efficiency simultaneously. No significant effective method has been found previously that can improve the location and recognition accuracy of events while ensuring the computing efficiency.