摘要
Optical fiber vibration is detected by the coherent optical time domain reflection technique. In addition to the vibration signals, the reflected signals include clutters and noises, which lead to a high false alarm rate. The ＂cell averaging＂ constant false alarm rate algorithm has a high computing speed, but its detection performance will be declined in nonhomogeneous environments such as multiple targets. The ＂order statistics＂ constant false alarm rate algorithm has a distinct advantage in multiple target environments, but it has a lower computing speed. An intelligent two-level detection algorithm is presented based on ＂cell averaging＂ constant false alarm rate and ＂order statistics＂ constant false alarm rate which work in serial way, and the detection speed of ＂cell averaging＂ constant false alarm rate and performance of ＂order statistics＂ constant false alarm rate are conserved, respectively. Through the adaptive selection, the ＂cell averaging＂ is applied in homogeneous environments, and the two-level detection algorithm is employed in nonhomogeneous environments. Our Monte Carlo simulation results demonstrate that considering different signal noise ratios, the proposed algorithm gives better detection probability than that of ＂order statistics＂.
Optical fiber vibration is detected by the coherent optical time domain reflection technique. In addition to the vibration signals, the reflected signals include clutters and noises, which lead to a high false alarm rate. The ＂cell averaging＂ constant false alarm rate algorithm has a high computing speed, but its detection performance will be declined in nonhomogeneous environments such as multiple targets. The ＂order statistics＂ constant false alarm rate algorithm has a distinct advantage in multiple target environments, but it has a lower computing speed. An intelligent two-level detection algorithm is presented based on ＂cell averaging＂ constant false alarm rate and ＂order statistics＂ constant false alarm rate which work in serial way, and the detection speed of ＂cell averaging＂ constant false alarm rate and performance of ＂order statistics＂ constant false alarm rate are conserved, respectively. Through the adaptive selection, the ＂cell averaging＂ is applied in homogeneous environments, and the two-level detection algorithm is employed in nonhomogeneous environments. Our Monte Carlo simulation results demonstrate that considering different signal noise ratios, the proposed algorithm gives better detection probability than that of ＂order statistics＂.
