Navigation System Design using Adaptive Fuzzy Strong Tracking Kalman Filter

• Main Authors: Sheng-Hung Wang, 王聖鋐
• Format: Others
• Language: zh-TW
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/61139809486545522443

碩士 === 國立臺灣海洋大學 === 通訊與導航工程系 === 94 === 英文摘要 While employed in the Global Positioning System (GPS) receiver as the navigational state estimator, the extended Kalman filter (EKF) provides optimal solution in terms of minimum mean square error. To obtain good estimation solutions using the EKF approach, the designers are required to have good knowledge on both dynamic process and measurement models, in addition to the assumption that both the process and measurement are corrupted by zero-mean Gaussian white noises. In various circumstances where there are uncertainties in the system model and noise description, and the assumptions on the statistics of disturbances are violated since in a number of practical situations, the availability of a precisely known model is unrealistic due to the fact that in the modelling step, some phenomena are disregarded and a way to take them into account is to consider a nominal model affected by uncertainty. Furthermore, the GPS measurement characteristics may be varying with time due to change in external environments where the statistical properties of errors in the system cannot be treated as unchanged. The facts discussed above results in filtering performance degradation or even divergence. To prevent divergence problem using the EKF approach, the adaptive filter algorithm is usually considered. One of the methods called the strong tracking filter (STKF) was proposed. It is essentially a nonlinear smoother algorithm that employs suboptimal multiple fading factors, in which the softening factors are involved. However, traditional approach for selecting the softening factors heavily relies on personal experience or computer emulation. In order to resolve this shortcoming, a new approach called the adaptive fuzzy strong tracking Kalman filter (AFSTKF), in which the fuzzy logic reasoning system based on the Takagi-Sugeno fuzzy sysem is combined with the strong tracking Kalman filter, will be incorporated into the navigation systems as a dynamic model corrector to aid the EKF for real-time identification of nonlinear dynamics errors. The fuzzy reasoning system is constructed for obtaining different softening factors for the state estimates based on the time varying circumstance. By monitoring the residual mean and variance, the fuzzy logic adaptive controller is employed for dynamically adjusting the softening factors and model variance matrices according to the proposed fuzzy rule. Computer simulation will be conducted to show the effectiveness of the proposed algorithm as compared to those by traditional ones. Keypoint：Takagi-Sugeno fuzzy logic, Strong tracking Kalman filter, Global Position System (GPS), innovation.