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当载体处于高动态运动状态时,GPS接收机载波跟踪信号极易受到外部环境不确定因素的影响。若采用标准的无迹卡尔曼滤波(UKF),在先验的噪声统计特性与实际的噪声统计特性不相符时,状态估计性能将变差甚至发散。针对上述问题,提出采用主从式自适应UKF的算法(AUKF)。AUKF能自适应调整过程噪声方差,从而达到减小模型估计误差、抑制滤波发散的目的。Matlab仿真结果表明,在高动态下噪声统计特性发生变化时,基于AUKF的载波跟踪算法具有较好的稳定性。
When the carrier is in a state of high dynamic motion, the GPS receiver carrier tracking signal can be easily influenced by the uncertainties of the external environment. With the standard Unscented Kalman Filter (UKF), the state estimation performance will deteriorate or even diverge if the a priori noise statistics do not match the actual noise statistics. To solve the above problems, this paper proposes a master-slave adaptive UKF algorithm (AUKF). AUKF can adaptively adjust the process noise variance so as to reduce the model estimation error and suppress the filter divergence. Matlab simulation results show that the carrier tracking algorithm based on AUKF has good stability when the statistical characteristics of noise change under high dynamic.