基于交互式多模型滤波的船体变形惯性测量方法

基于惯性测量单元的匹配滤波算法是测量船体变形的发展趋势,然而在实际航行中,船体变形模型参数是未知或存在不确定性,模型参数的这一特性对滤波估计结果影响较大。针对此问题,利用"速度+角速度"匹配算法分析了模型参数未知对滤波估计效果的影响,引入交互式多模型卡尔曼滤波方法,利用不同模型参数的似然函数进行概率分配。最后通过仿真对提出的方法进行了验证,结果表明,与传统卡尔曼滤波相比,估计精度提高了5%~10%,收敛时间提高了1倍,动态变形角的收敛时间在10 s以内,静态变形角的收敛时间在5s以内,提高了系统的环境适应性。

Inertial measurement method of ship deformation based on IMM filtering

Matching filtering algorithm based on inertial measurement unit is the development trend to overcome the deformation of ship hull.The Kalman filter requires that the system model parameters are known accurately,however the parameters of the actual ship hull deformation model are unknown or uncertain.To solve this problem,an interacting multiple model (IMM) Kalman filtering method is proposed based on "velocity plus angular velocity" model matching,and the estimated residuals of each model are used to calculate the probability of the corresponding model.Simulation results show that the proposed algorithm has such features as fast convergence,good stability and high accuracy.The accuracy of the IMM algorithm is increased by 5％～10％ than that of the Kalman filter,and the convergence time is within 10 s,which improve the environmental adaptability of the system.