基于次优区间卡尔曼滤波的机器鱼跟踪方法

目前,自主水下航行器(Autonomous Underwater Vehicle,AUV)研究的重点集中在跟踪定位、精确制导和返坞等领域。机器鱼已成为AUV在智能教育、民用与军事等方面的应用热点。从非线性跟踪分析中发现,区间卡尔曼滤波算法虽然包含了一切可能的滤波结果,但范围比较宽,也比较保守,而且区间数据向量在实现之前是不确定的。文中提出了一种次优区间卡尔曼滤波优化算法。次优区间卡尔曼滤波方案用区间矩阵的逆 代替 其最坏逆,比标准区间卡尔曼滤波更逼近状态方程和测量方程的非线性过程,提高了标称动态系统模型的精确度,改善了跟踪系统的速度与精度。蒙特卡洛仿真实验结果表明,次优区间卡尔曼滤波算法的最优轨迹优于区间卡尔曼滤波方法及标准的卡尔曼滤波方法。

Robotic Fish Tracking Method Based on Suboptimal Interval Kalman Filter

Research of autonomous underwater vehicle (AUV) focuses on tracking and positioning,precise guidance and return to dock,and so on.The robotic fish of AUV has become a hot application in intelligent education,civil and military and so on.From the nonlinear tracking analysis of robotic fish,it is found that the interval Calman filtering algorithm contains all possible filtering results,but the range is wide and relatively conservative,and the interval data vector is uncertain before implementation.This paper proposed a ptimization algorithm of suboptimal interval Kalman filtering.Suboptimal interval Kalman filtering scheme uses the inverse of interval matrix instead of its worst inverse,and it is more approximate to nonlinear state equation and measurement equation than the standard interval Kalman filter,increasing the accuracy of the nominal dynamic system model,and improving the speed and precision of tracking system.Monte-Carlo simulation results show that the optimal trajectory of suboptimal interval Kalman filtering algorithm is better than that of the interval Kalman filtering method and the standard filter method.