基于IMM-EKF的高动态“北斗”导航信号频率估计算法

发布时间：2018-03-10 09:41

本文选题：“北斗”卫星导航系统　切入点：频率估计　出处：《电讯技术》2017年08期 　论文类型：期刊论文

【摘要】：高动态环境下的"北斗"导航信号含有较大的多普勒频率及其变化率,传统锁相环(PLL)在跟踪时难以保证较高的跟踪精度。在分析高动态环境下"北斗"信号模型的基础上,提出了一种基于交互式多模型-扩展卡尔曼滤波(IMM-EKF)的自适应滤波算法,对载波相位及其高阶分量进行估计。IMM-EKF采用多个跟踪模型来解决滤波过程中单个模型不准确的问题,并结合改进的SageHusa自适应算法,在线估计和修正过程噪声及测量噪声的统计特性,增强了滤波的稳定性。仿真结果表明,IMM-EKF相比于PLL和EKF,估计精度更高,算法稳定性更强。
[Abstract]:The "Beidou" navigation signal in high dynamic environment contains a large Doppler frequency and its changing rate, so it is difficult for the traditional PLL to ensure a high tracking accuracy when tracking. Based on the analysis of the "Beidou" signal model in the high dynamic environment, An adaptive filtering algorithm based on interactive multi-model-extended Kalman filter (IMM-EKF) is proposed, in which the carrier phase and its high-order components are estimated by using multiple tracking models to solve the problem of inaccuracy of single model in the filtering process. Combined with the improved SageHusa adaptive algorithm, the statistical characteristics of process noise and measurement noise are estimated and corrected on line, and the stability of the filter is enhanced. The simulation results show that the estimation accuracy of IMM-EKF is higher than that of PLL and EKF, and the stability of the algorithm is stronger than that of PLL and EKF.
【作者单位】：装备学院光电装备系;装备学院科研部;
【分类号】：TN967.1

【相似文献】