一种用于高动态GPS频率估计的滤波算法

Filtering algorithm used for high dynamic GPS frequency estimation

针对常用高动态GPS(G lobal Positioning System)频率估计算法扩展卡尔曼滤波(EKF,Extended Kalman Filter)的缺陷,提出了一种新的称为简化无迹高斯粒子滤波(SUGPF,Simplified Unscented Gaussian Particle Filter)的算法.SUGPF将卡尔曼滤波(KF,Kal-man Filter)、无迹卡尔曼滤波(UKF,Unscented Kalman Filter)与高斯粒子滤波(GPF,GaussianParticle Filter)三者相结合.在时间更新阶段,用KF的方法更新预测分布;在测量更新阶段,用UKF的方法得到重要采样函数,并用GPF的方法更新后验分布.仿真结果表明:与EKF和UKF相比,SUGPF性能更优越,功能更全面,在高斯与非高斯观测噪声环境下均能取得与GPF类似的良好性能,并且其计算复杂度低于GPF.

Aiming at the drawbacks of the extended Kalman filter （EKF） which is the widely used GPS frequency estimation algorithm in high dynamic circumstance, a novel filtering algorithm called simplified unscented Gaussian particle filter （SUGPF） was proposed. The SUGPF is the combination of Kalman filter （ KF）, unscented Kalman filter （UKF） and Gaussian particle filter （GPF）. In time update step, KF methodology was used to update the predictive distributions. In measurement update step, the UKF methodology was used to obtain the important sampling function, and the posterior distributions were updated by using the methodology of GPF. The simulation results indicate that the SUGPF has improved performance and versatility over the EKF and UKF, under both Gaussian and non-Gaussian observation noise condition, SUGPF can achieve good performance which is similar as that of the GPF, and the computational complexity of the SUGPF is lower than that of the GPF.