基于半球谐振陀螺的捷联惯性姿态系统的一种粒子滤波方法

An Extended Kalman Particle Filtering Algorithm for HRG-Based Strapdown Inertial Attitude Determination System

为了抑制积分运算带来的捷联惯性姿态累积误差,根据姿态角三角函数关系引入横滚、俯仰角的三个替换变量作为姿态变量,并根据系统姿态变量之间的约束方程和运动体的前向速度测量提出了基于半球谐振陀螺的捷联惯性姿态确定系统的一种扩展卡尔曼粒子滤波算法。采用基于国产半球谐振陀螺的捷联惯性测量组合进行实验与仿真,与标准卡尔曼滤波算法进行了比较。实验与仿真结果表明:该算法有效地提高了定姿性能。作为高可靠性姿态确定系统的备用算法,该算法尤其适用于长寿命的小型航天器。

To prevent the accumulated error of the inertial attitude solution, according to the trigonometric function relationship between attitude angles, three variables replaced the roll and the pitch as state variants. With the restrained equation among state variables and the forward motion speed, one extended Kalman particle filter （EKPF） algorithm was proposed to solve the attitude angles. The EKPF algorithm was applied to our inertial attitude reference unit based on the hemispherical resonator gyroscope （HRG）, which was made in China. Simulation results indicated that the root mean square error values of the roll angle and the pitch angle of the proposed EKPF were less than the half of those of the standard extended Kalman filter （EKF）.