A Guided Multiobjective Optimization Method for Attenuation Calibration in Distributed Fiber Sensing

Optical fibers serve as both signal transmission channels and sensing units in distributed fiber sensing systems (DFSs). The sensor’s detection signal may be adversely affected by multiple forms of attenuation due to unavoidable bending and splices during fiber installation. Ensuring signal reliability requires precise calibration of signal attenuation. The attenuation calibration of multiple measurement points along the optical fiber is transformed into an error-related large-scale multiobjective optimization problem and resolved via a priori knowledge guiding evolutionary algorithm, which makes full use of the redundancy of data from adjacent measurement points and ensures the accuracy of each measurement point. As a means of introducing a priori knowledge to guide the search, the dynamic beta distribution of the population initialization in the evolutionary algorithm is used to achieve better convergence performance. To assess the calibration efficiency, experiments are conducted at various temperatures, revealing that the calibration at a temperature of $0~^{\circ }$ C led to significant reductions of 24.29%, 0.23%, and 99.91% for each objective function. Moreover, postcalibration temperature measurements at multitemperature points and the dynamic temperature measurement exhibit reduced mean absolute error (MAE), enhancing the accuracy of temperature measurements. This research offers a fresh perspective on the utilization of optimization algorithms, presenting an innovative approach to simultaneously calibrate multiple measurement points.