一种新的高动态全球定位系统频率估计算法

A novel GPS frequency estimation algorithm inhigh dynamic circumstances

针对常用高动态全球定位系统频率估计算法扩展卡尔曼滤波(EKF)由于对模型进行简单线性化而造成的估计精度低与失锁门限高等缺陷,引入了一种新的线性滤波算法unscented卡尔曼滤波(UKF)进行频率估计.该算法不需要对非线性模型进行线性化,而是利用一系列Sigma采样点,通过unscented变换(UT)来进行状态与协方差阵的递推与更新.仿真实验结果表明新算法的估计精度远高于EKF,失锁门限也比EKF低约1 dB,估计性能得到了改善.

Low estimation precision and high loss-of-lock threshold are the two important drawbacks of the extended Kalman filter （EKF） which is the widely used GPS frequency estimation algorithm in high dynamic circumstances, caused by linearizing all nonlinear models. To resolve the problems of EKF, the unscented Kalman filter （UKF） which is a new kind of linear filter is introduced to estimate frequency. Instead of the linearization steps required by the EKF, a series of Sigma points and unscented transform （UT） are used to predict and update the state vector and covariance. Simulations indicate that the estimation precision of UKF is greatly improved compared with that of EKF, that the loss-of-lock threshold is about 1 dB lower than that of EKF, and that the estimation performance is improved.