改进ERTS平滑算法在位置跟踪中的应用研究

Application Research on the Improved ERTS Smoothing Algorithm in Position Tracking

针对标准ERTS平滑算法在位置和姿态估计中计算复杂、效率低、精度不高等问题,提出了利用奇异值分解法改进ERTS平滑算法优化位置和姿态数据的新方法。对系统采集到的位置和姿态信息进行前向扩展卡尔曼滤波,降低系统噪声的初步影响;对滤波后的均方误差阵进行奇异值分解,并降低后向递推增益和预测值计算量,提高了预测精度,有效增强了系统的抗干扰性和稳定性。Turtlebot移动机器人平台的试验效果证明该算法在位置和姿态估计中的高效性和稳定性。

To overcome the disadvantages of standard ERTS smoothing algorithm in position and attitude estimation, e. g. , complexity, low efficiency, and poor precision, etc. , the new improved ERTS smoothing algorithm by adopting singularity valve decomposition is proposed for optimizing position and attitude data. After forward extended Karman filtering for the information of position and attitude collected in the system, the initial impact ofthe system noise is reduced; the singularity value decomposition is conducted for the MSE matrix after filtering, thus the backward recursion gain and the calculated amount of the predicted valueare decreased, and the prediction accuracy is improved ; as well as the anti-interference and stability of the system are effectively strengthened. The experimental result on Turtlebot mobile robot platform verifies the high effectiveness and stability of this algorithm in position and attitude estimation.