基于模糊逻辑四元数的平方根UKF算法

Square Root UKF Algorithm Based on Fuzzy Logical Quaternion

针对目标跟踪估计算法中,利用传感器离散观测建模并估计目标连续状态,进行导航解算时滤波精度低,及传统四元数扩展卡尔曼滤波算法无法满足目标姿态测量精度要求等问题,文中提出一种基于模糊逻辑四元数的平方根UKF姿态估计算法.该算法将四元数作为模糊逻辑UKF滤波器状态,应用测量的角速率完成滤波器的时间更新及量测更新,采用模糊逻辑的平方根协方差形式作为更新参数,既降低了算法的计算量和复杂度,又保证了数值的稳定性.文中还以近地卫星为例,在Matlab和C++软件上进行了仿真实验.实验结果表明:文中所提算法可保证对机器人和特征的姿态控制估计精度;可对目标实时跟踪定位并进行量测更新,抑制了姿态误差发散问题.

An attitude estimation algorithm based on fuzzy logic square root quaternion UKF is proposed in order to solve the problems such as the filtering accuracy of target continuous state estimation by discrete sensors observation is too low for navigation solution and the traditional quaternion extended Kalman filtering algorithm can not meet the precision requirement of target attitude measurement. The algorithm takes quaternion as the state of fuzzy logic UKF filter, and the measured time rate is used to complete the time update and measurement update of the filter. The square root covariance form of fuzzy logic is used as the update parameter, which reduces the calculation amount and complexity of the algorithm, and ensures the stability of the value. Taking near-earth satellite as an example, simulation experiments were carried out on Matlab and C++ software. The experimental results show that the proposed algorithm could guarantee the accuracy of attitude control for robots and features. It can also track and locate the target in real time and update the measurement, which suppress the problem of attitude error divergence.