| 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。 |
| 关键词: “北斗”卫星导航系统 频率估计 高动态 Sage-Husa算法 自适应滤波 |
| DOI：10.3969/j.issn.1001-893x.2017.08.012 |
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| 基金项目: |
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| An IMM-EKF based frequency estimation algorithm for high dynamic BDS signals |
| SHI Youmu,WANG Yuanqin |
| (a.Department of Photoelectric Equipment;b. Department of Research，The Academy of Equipment,Beijing 101416,China) |
| Abstract: |
| 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 circumstances,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. |
| Key words: Beidou navigation satellite system(BDS) frequency estimation high dynamic Sage-Husa algorithm adaptive filter |
《电讯技术》编辑部 