Unscented Kalman filter with high dynamic modeling for GNSS carrier tracking
This paper preliminarily investigates the application of unscented Kalman filter (UKF) approach with high dynamic modeling for Global navigation satellite system (GNSS) carrier tracking. Many estimation problems, including the GNSS carrier tracking, are actually nonlinear. Although it has been common that additional fictitious process noise can be added to the system model, however, the more suitable cure for non convergence caused by unmodeled states is to correct the model. For the nonlinear estimation problem, alternatives for the classical model-based extended Kalman filter (EKF) can be employed. The UKf is a nonlinear distribution approximation method, which uses a finite number of sigma points to propagate the probability of state distribution through the nonlinear dynamics of system. The UKf exhibits superior performance when compared with EKf since the series approximations in the EKF algorithM can lead to poor representations of the nonlinear functions and probability distributions of interest. GNSS carrier tracking using the proposed approach will be conducted to validate the effectiveness of the proposed strategy. The performance of the UKf with high dynamic model will be assessed and compared to those of conventional methods.
Wenjing Wang Shuai Han Weixiao Meng
Communication Research Center Harbin Institute of Technology Harbin, Heilongjiang, China
国际会议
The 2nd Joint FWOCNT International Workshop 2009(第二届前沿无线通信、光通信与网络技术国际研讨会暨第十四届青通会论坛)
大连
英文
39-43
2009-07-24（万方平台首次上网日期，不代表论文的发表时间）