The Analysis of Adaptive Constraint Filtering Method and Its Application to Ill-conditioned GPS Positioning
Date Issued
2010
Date
2010
Author(s)
Chang, Tsai-Hsin
Abstract
To deal with the estimation problem for systems subject to constraints while the corresponding noise processes are not completely known, the adaptive constraint-filtering method is proposed in this study. As having been shown in [1], the constraint-filtering method can accommodate the soft constraint in the filtering process for a nonlinear dynamic system. The method is based on the knowledge of the modeling noise and the sensor noise, which may not be practical. In this study, the fuzzy innovation adaptive estimation (FIAE) approach is proposed to deal with the unknown noise processes. Furthermore, for land vehicles, it may be assumed that the speed of the vehicle is not varying significantly, so that the constraint-filtering method with soft constraint is then applied to solve the ill-conditioned GPS positioning problem.
For GPS positioning, it is normally required that there be at least four GPS satellites in view. However, due to the frequent blockage of signals in urban environment, it is difficult to meet that requirement so that the operation of GPS receiver may be interrupted. How to deal with this problem so that the service can be continuous is also the main theme of this study. The pseudorange predictor utilizes the current user and satellite’s positions and velocities to estimate the next time satellite’s pseudorange. In addition, when the receiver observes only two satellites, the receiver clock bias predictor may be used to estimate the clock bias. If there are three satellites in view, the altitude-hold algorithm is developed to provide additional information under the assumption that the altitude of the vehicle is approximately a constant, which is deemed appropriate for urban applications. The integration of these methods yields a successful algorithm to manage the ill-conditioned positioning problem if the number of visible satellites is insufficient.
The basic Monte Carlo method is applied to simulation, in order to assess and compare the performance of the various filters, such as the Kalman filter (KF), the adaptive Kalman filter (AKF), the constraint-filtering (CF), and the adaptive constraint-filtering (ACF). The simulation results show that the adaptive constraint-filtering method is evidently better than the other filters. From both static and dynamic experimental results, it is shown that the proposed methodology indeed gives rise to an effective scheme which can sustain the service for a few minutes even if there is no satellite in view at all. The adaptive constraint-filtering method can enhance the accuracy and outperform the Kalman filtering method significantly.
Subjects
Adaptive constraint-filtering method
GPS
Pseudo-innovation sequence
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-99-D93543007-1.pdf
Size
23.53 KB
Format
Adobe PDF
Checksum
(MD5):c8b15c7f8d5aafae5da05ab09e6d8e92