Fuzzy Strong Tracking Nonlinear Filter for Ultra-Tight GPS/INS Integration
| Autor: | Ting-Yu Lee, 李庭宇 |
|---|---|
| Rok vydání: | 2010 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 98 Ultra-tight integration is becoming more and more important in the future. Many advantages about the robustness in the high dynamics and the immunity to the jamming and interference. Due to these advantages, the next generation integrated GPS/INS navigation systems will be based on the ultra-tightly coupled system. The UKF employs a set of sigma points by deterministic sampling, such that the linearization process is not necessary, and therefore the error caused by linearization as in the traditional extended Kalman filter (EKF) can be avoided. An ultra-tight navigation integration processing scheme, called the strong tracking unscented Kalman filter (STUKF), is based on the combination of unscented Kalman filter (UKF) and strong tracking filter (STF). As a type of adaptive filter, the STF is essentially a nonlinear smoother algorithm that employs suboptimal multiple fading factors, in which the softening factors are involved. In order to resolve the shortcoming in traditional approach for selecting the softening factor through personal experience or computer simulation, a novel scheme called the fuzzy strong tracking unscented Kalman filter (FSTUKF) is presented where the Fuzzy Logic Adaptive System (FLAS) is incorporated for determining the softening factor. This paper carries out a fuzzy strong tracking unscented Kalman filter (FSTUKF) application approach for the ultra-tight GPS/INS integration. Through analyzing the relationship between GPS (I&Q) correlator outputs and navigation states (position and velocity), the EKF based system was presented an the baseline design. Finally, the application method of FSTUKF for the ultra-tight integration system is proposed. The proposed FSTUKF algorithm shows promising results in estimation accuracy when applied to the ultra-tight integrated navigation system application, as compared to the EKF, UKF and STUKF approaches. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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