利用人工鱼群算法对光纤陀螺随机漂移建模

摘要

Using historical data to accurately set up FOG random drift model plays an important role in improving the precision of FOG strapdown inertial navigation system. This paper details the artificial fish swarm algorithm(AFSA) and improved artificial fish swarm algorithm(IAFSA), and gave the steps and methods for random signal modeling by AFSA. Models of FOG random drifts are set up by the traditional time series analysis, AFSA and IAFSA, respectively. The modeling results show that, the FOG Random Drift Model by AFSA is accurate and its precision is 1.5% higher than that of the traditional time series analysis. AFSA and IAFSA are both superior to the traditional time series analysis method in complexity or precision of modeling. The model by IAFSA is more accurate than AFSA, and its convergence speed is faster than that by AFSA.%利用历史数据,对先纤陀螺随机漂移进行准确建模,对提高光纤捷联惯导系统的精度具有十分重要的意义.文中详细介绍了人工鱼群算法(Artificial Fish Swarm Algorithm,AFSA)和改进人工鱼群算法( Improved Artificial Fish Swarm Algorithm,IAFSA),给出了AFSA对随机信号建模的详细步骤和方法,分别应用传统的时间序列分析方法、人工鱼群算法、改进人工鱼群算法对光纤陀螺的随机漂移进行了建模.建模结果表明,AFSA对光纤陀螺随机漂移建模准确,比传统时间序列分析建模精度提高1.5％,IAFSA建模精度比AFSA建模精度更高,其收敛速度也更快.无论是从建模复杂度上,还是在建模精度上,AFSA和IAFSA均优于传统的时间序列分析方法,IAFSA是一种收敛速度更快、建模精度更高的光纤陀螺随机信号建模方法,