Navigation and Control of Unmanned Vehicles (II)
Date Issued
2000-11-30
Date
2000-11-30
Author(s)
DOI
892212E002084
Abstract
To perform the navigation of an unmanned vehicle,
various sensors, such as GPS, INS, compass, encoder,
etc, can be used. Depending on their characteristics,
different sensors may have different advantages. For
example, GPS may be more sensitive to low-freqency
noise, while INS is more susceptible to high-frequency
noises. In order to integrate these sensors, the algorithm
of data fusion along with the Kalman filter may
be adopted. However, there are some issues having
to be tackled. First, the initial setting of the algorithm
must be given. Secondly, if the assumption
of independence in the Kalman filtering is not valid,
it is necessary to deal with dependent processes. In
this report, an algorithm of determining the initial
settings, including the error covariance, the process
noise covariance and the measurement noise covariance,
is proposed. On the other hand, the covariance
intersection algorithm is used to solve the problem
regarding the dependence of the information. The
combination of these strategies is then used to design
the fusion INS-GPS system for the navigation
of a vehicle. The experimental results showed that
the algorithm is more robust comparing with classical
Kalman filtering algorithm.
Publisher
臺北市：國立臺灣大學應用力學研究所
Type
report
File(s)![Thumbnail Image]()
Loading...
Name
892212E002084.pdf
Size
231.29 KB
Format
Adobe PDF
Checksum
(MD5):f6c07ef14676e3ef1d00c33eccbca325