基于WLS-KF的GPS非线性动态滤波研究

摘要

为了提高动态定位精度,将卡尔曼(KF)算法应用到GPS非线性动态定位解算中,提出加权最小二乘-卡尔曼滤波(WLS-KF)算法.通过加权最小二乘(WLS)算法得到近似的线性化模型,再将KF算法应用到这个线性化模型进行校正.因此既保持了KF算法能够对系统状态进行最优估算的优点,同时对各个测量值进行了联系制约,具有更高的精度.结果表明,这种方法精度介于EKF和UKF之间,且实现容易,预测可靠,具有实际应用价值.%In order to improve the positioning accuracy, the weighted least squaresKalman filter(WLS-KF) algorithm is proposed by applying Kalman filter (KF) into GPS nonlinear dynamic filter. An approximate linear system is formed from the WLS algorithm and regulated by KF. Thus it can keep the advantages of that the KF algorithm can optimally estimate the system status, restrict and contact each measured value. The results show that the accuracy range of this algorithm between EKF and UKF has reliable prediction and practical applications.