一种改进的高动态GPS载波跟踪算法研究

An Improve Algorithm for Tracking GPS Carrier Frequency in High Dynamic Circumstances

针对常用的高动态GPS载波跟踪算法(EKF)带来的频率估计低和调制数据跳变检测问题,首先提出了一种新的解决数据跳变的方法,其次提出了一种将UKF算法和IEKF算法相结合的改进UKF算法进行频率估计,由于引入了简化球形分布Sigma点UT变化,使Sigma点的数量大量减少,从而在保持了基本UKF算法计算量小的优点的基础上,由于滤波值是通过迭代扩展的卡尔曼滤波机制得到,进而更新值能更准确的逼近非线性系统状态概率密度函数,所以比基本UKF算法具有更高的精度。

Low estimation precision and detecting data transition are the two important drawbacks of the extended Kalman filter(EKF) which is the widely used GPS frequency estimation algorithm in high dynamic circumstances. Firstly A new method of detecting data transition is presented. Then a algorithm which integrates UKF algorithm with IEKF algorithm is presented. The number of Sigma decreases sharply because a better behaved sigma point selection strategy( spherical simplex unscented transformation) was introduced. Which Can basically keep the characters of UKF algorithm-easy to be realized and faster convergence,and the theory of iteration can approach the nonlinear system state probability density function more accurately. Finally the results also have higher accuracy to the UKF.