The project for the road information updating in geographical information system by using kinematic GPS method has
been finished for county and country roads in China. The kinematic positioning results of commercial GPS navigation
software, differential GPS positioning and an adaptively robust filtering are compared and analyzed. A synthetic adaptive
factor by combining two kinds of adaptive factors is proposed for adjusting the contributions of kinematic model
information and measurements on the state estimates, one is constructed with the statistics of discrepancy of kinematic
model predicted state and estimated state from the measurements, and the other is set up by using the statistic of
predicted residual vector. It is shown by experiments that the adaptively robust filtering with synthetic adaptive factor is
valid in the cases with or without adequate GPS measurements. The calculation procedure is similar to the standard
Kalman filter and navigation results are robust in controlling the influences of the outliers of the GPS measurements and
kinematic state disturbing of the vehicle. The accuracy of adaptively robust filtering with only the GPS pseudo-ranges
can meet the requirements of the road information updating for 1:250000 digital maps.
Precise Orbit Determination (POD) of Low Earth Orbiter satellites (LEOs) based on Point Positioning (PPP) technique utilizing dual-frequency spaceborne GPS observations has become one of the best POD methods at present. Quality control of raw spaceborne GPS observations is very complex but critical for achieving high orbiting accuracy. Among the various existing methods for detecting outliers, the majority-voting algorithm used in Bernese 5.0 is a very efficient one. However, the performance of this algorithm may be affected by choice of the input parameters such as standard deviation for arranging the observations into groups, standard deviation for setting the rejection threshold and factors α and β whose values are often manually selected by experience. If the threshold is set too high, the relatively small outliers might not be found; on the contrary, more observations might be excluded and no solution could be computed for a particular epoch if the number of satellites per epoch is set to be smaller than 4. To overcome these limitations, this paper presents an improved majority-voting algorithm, which determines the some options by iteration instead of manual selection, and utilizes QUasi-Accurate Detection of outliers (QUAD) to correct the marked observations by this algorithm. The determined orbit of LEO using this new algorithm is continuous and smooth. Therefore, the improved majority voting is feasible and efficient.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.