基于卡尔曼滤波的云台自适应姿态优化算法

Adaptive Attitude Optimization Algorithm of PTZ Based on Kalman Filter

姿态角的解算精度是影响云台工作性能的关键因素。该文基于四元数和三维旋转的数学原理,对扩展卡尔曼滤波器进行改进,提出自适应变噪声数学模型,并利用非线性互补滤波器对实验结果进行并行数据融合。该文利用Matlab进行仿真实验比较常规EKF算法和该文设计的改进型姿态融合算法的解算精度。当姿态角发生摇摆运动时,改进型姿态融合算法的动态精度提高了30%左右;当姿态角恒定来模拟云台锁定目标时,改进算法的静态误差和误差均方根都有了明显降低。结果表明,该文提出的改进型滤波算法可以有效提高系统的解算精度,增强系统的稳定性。

The calculation accuracy of attitude angle is the key factor affecting the working performance of PTZ.Based on the mathematical principles of quaternions and three-dimensional rotation,this paper proposes an adaptive variable noise mathematical model to improve the performance of the extended Kalman filter and uses the nonlinear complementary filter to fuse the results in parallel.In this paper,Matlab is used for simulation experiments to compare the solution accuracy of the classical EKF algorithm and the improved attitude fusion algorithm designed in this paper.When the attitude angle swings,the dynamic accuracy of the improved attitude fusion algorithm is improved by about 30%;When the attitude angle is constant to simulate the PTZ locking target,the static error and root mean square error of the improved algorithm are significantly reduced.The results show that the improved filtering algorithm proposed in this paper can effectively improve the accuracy of the system and enhance the stability of the system.