基于IMM-EKF的高动态“北斗”导航信号频率估计算法

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Sage-Husa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.

An IMM-EKF based frequency estimation algorithm for high dynamic BDS signals

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.