Study on CA-CFAR Algorithm Based on Normalization Processing of Background Noise for HI of Optical Fiber

Abstract

Optical fiber pre-warning system (OFPS) is often used to monitor the occurrence of disasters such as the leakage of oil and natural gas pipeline. It analyzes the collected vibration signals to judge whether there is any harmful intrusion (HI) events. At present, the research in this field is mainly focused on the constant false alarm rate (CFAR) methods and derivative algorithms to detect intrusion signals. However, the performance of CFAR is often limited to the actual collected signals distribution. It is found that the background noise usually obeys non-independent and identically distribution (Non-IID) through the statistical analysis of acquisition signals. In view of the actual signal distribution characteristics, this paper presents a CFAR detection method based on the normalization processing for background noise. A high-pass filter is designed for the actual Non-IID background noise data to obtain the characterization characteristic. Then, the background noise is converted to independent and identically distribution (IID) by using the data characteristic. Next, the collected data after normalization is processed with efficient cell average constant false alarm rate (CA-CFAR) method for detection. Finally, the results of experiments both show that the intrusion signals can be effectively detected, and the effectiveness of the algorithm is verified.

Signal processing algorithm of ship navigation radar based on azimuth distance monitoring

“…In the constant false alarm monitor, CA-CFAR [17] is selected to suppress rain and snow clutter, and the calculation formula is:…”