摘要
针对DR系统中利用扩展卡尔曼滤波（EKF）方法解决非线性问题在算法复杂性上的缺陷和精度上的不稳定性，从数据预先处理的角度出发合理规避线性化过程，提出了一种充分利用现成经典卡尔曼滤波公式的行之有效的方法，就是数据进入滤波器之前就预先进行处理，获得每一组量测值按照极坐标-笛卡儿坐标的转换方式转为相应的位置信息，然后按照标准的卡尔曼滤波公式构建滤波器，进行状态的最优估计。计算机仿真结果证明了这种算法的有效性。
In dead reckoning system, the limitation of complexity and instability of precision are appeared for nonlinear problem solved using Extended Kalman Filtering（EKF）. A effective method is provided using fully classical kalman filtering formula, in order to avoiding linearization process from data pre-processing. The data is processed before it is input filter. Each measuring observation is transformed from polar coordinates into Cartesianism coordinates, then the filtering is done according to standard Kalman filtering in order to state best estimation. The validity of the method is tested by computer simulation.
基金
国家自然科学基础资助项目（项目编号：49901013）,中国博士后基金资助项目.
