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。
The Beidou navigation satellite system（ BDS） signal incorporates a large Doppler frequency and its rate of change in the presence of high dynamics. The traditional phase-locked loop（PLL） is incapable of tracking it with high accuracy. According to the analysis of the BDS signal model under high dynamic circum-stances,an adaptive interacting multiple model-extended Kalman filter（ IMM-EKF） algorithm is proposed to estimate the phase and its high order derivatives. In IMM-EKF algorithm,several tracking models are used to deal with the inaccuracy of one single model. Combined with improved Sage-Husa algorithm,it is able to on-line estimate and adjust the statistical properties of measurement noise and process noise. Simulation results indicate that this algorithm is more accurate and stable than traditional PLL and EKF.
出处
《电讯技术》
北大核心
2017年第8期923-931,共9页
Telecommunication Engineering
