# The Fast Detection and Identification Algorithm of Optical Fiber Intrusion Signals
## 1 Introduction  
Optical fiber sensors have many advantages such as anti-electromagnetic interference, passive, good electrical insulation, high sensitivity, and adaptability to a wide range of complex environment [1; 2; 3; 4; 5]. Therefore, they are widely used in security pre-warning for border lines, airports, military bases, power plants, and oil pipelines [6]. The principle of \(\Phi\)-OTDR optical technology is adapted in Optical Fiber Pre-Warning System (OFPS), the light pulse is injected from one end of the fiber, and the light detector is used to detect the backward Rayleigh scattered light. When the fiber link has a disturbance, the light intensity at the corresponding position changes, and the same time is measured before and after the measurement. The difference in energy of the position detects the vibration position. When an intrusion occurs above the ground, the vibration is transmitted to the buried fiber and causes it to vibrate. The purpose of OFPS is to detect intrusion events with high resolution and to identify the detected intrusions with high accuracy. How to improve the real-time performance of OFPS during data processing of optical fiber intrusion signals is a problem. In recent years, with the continuous improvement of random signal processing techniques and pattern recognition methods, the research into optical fiber intrusion signals detection and recognition algorithms has achieved significant progress. For intrusion signals detection, the Constant False Alarm Rate (CFAR) algorithms effectively overcome the adverse effect caused by a large number of non-stationary random interference signals, and greatly improve the detection performance of OFPS [7; 8; 9; 10]. However, in practical applications, it is found that there are a large number of false alarms in CFAR detection results. In the embedded processing, the interruption time is 1 s. When the data sampling time reaches 1 s, the timer interruption is entered, and the sampled data is placed in thebuffer. After reading the data of the buffer, continue the next second sampling. The CFAR algorithms need to process a large amount of data in a unit of time. If the data is not processed completely within 1 s, the remaining data will be left to be processed in the next second. This will lead to disordered data and will seriously affect the real-time performance and accuracy of the system. Therefore, this paper proposes a single-channel detection method, and uses the dislocation calculation method to further optimize the performance of the algorithm. It has been observed that the false alarm rate of this single-channel detection method is lower than CFAR. For the identification of intrusion events, the traditional analysis method is to extract the signal features in the time domain [11], frequency domain [12] or time-frequency domain [13; 14; 15; 16; 17] and perform the recognition based on features. However, these methods are limited to general-purpose computers and the processing speed is slow. In practical applications, embedded implementations can not only run outdoors, but also have the characteristics of high real-time performance, simple operation, and portability. With the development of embedded technology and OFPS, the outdoor operations demand of OFPS is more and more strict, and the requirements for real-time processing are getting higher and higher. Therefore, this paper uses Fast Fourier transform (FFT) to extract the features of optical fiber intrusion signals and identify them, which can achieve the purpose of rapid identification and meet the real-time performance of the system. Based on this, this paper presents a new fast algorithm for the detection and identification of optical fiber intrusion signals. The algorithm is simple and easy to implement, and the computational complexity is much lower than the traditional methods. The dislocation summation of the detection part and FFT of the identification part both greatly improve the real-time performance of the system.