碩士 / 國立臺灣海洋大學 / 通訊與導航工程系 / 94 / Abstract
The Global Positioning System (GPS) and inertial navigation systems (INS) have complementary operational characteristics and the synergy of both systems has been widely explored. Most of the present navigation sensor integration techniques are based on Kalman filtering estimation procedures. For obtaining optimal (minimum mean square error) estimate, the designers are required to have exact knowledge on both dynamic process and measurement models. In this paper, a mechanism called PSO-RBF, which combines Radial Basis Function (RBF) Network and Particle Swarm Optimization (PSO), for predicting the errors and to filtering the high frequency noise is proposed. As a model nonlinearity identification mechanism, the PSO-RBF can implement the on-line identification of nonlinear dynamics errors such that the modeling error can be compensated. The PSO-RBF is applied to the loosely-coupled GPS/INS navigation filter design. The proposed approach has demonstrated significant performance improvement in comparison with the standard
Kalman filtering method.
KEYWORDS:
Kalman filter(KF), Radial Basis Function (RBF), Particle Swarm Optimization(PSO), Integrated Navigation Systems(INS), Global Positioning System(GPS), Loosely-Coupled INS/GPS Filter
| Identifer | oai:union.ndltd.org:TW/094NTOU5300008 |
| Date | January 2006 |
| Creators | Jyh-Jeng Chen, 陳志正 |
| Contributors | Dah-Jing Jwo, 卓大靖 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 114 |
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