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Medium to Long Range Kinematic GPS Positioning with Position-Velocity-Acceleration Model Using Multiple Reference Stations.

Hong CK, Park CH, Han JH, Kwon JH - Sensors (Basel) (2015)

Bottom Line: In order to obtain precise kinematic global positioning systems (GPS) in medium to large scale networks, the atmospheric effects from tropospheric and ionospheric delays need to be properly modeled and estimated.The performance of the proposed algorithms is validated by analyzing and comparing the results with those from reference values.The results show that reliable and comparable solutions in both position and kinematic acceleration levels can be obtained using the proposed algorithms.

View Article: PubMed Central - PubMed

Affiliation: Department of Geoinformatics Engineering, Kyungil University, 50 Gamasilgil, Kyeongsan, Gyeongbuk 712-701, Korea. ckhong@kiu.ac.kr.

ABSTRACT
In order to obtain precise kinematic global positioning systems (GPS) in medium to large scale networks, the atmospheric effects from tropospheric and ionospheric delays need to be properly modeled and estimated. It is also preferable to use multiple reference stations to improve the reliability of the solutions. In this study, GPS kinematic positioning algorithms are developed for the medium to large-scale network based on the position-velocity-acceleration model. Hence, the algorithm can perform even in cases where the near-constant velocity assumption does not hold. In addition, the estimated kinematic accelerations can be used for the airborne gravimetry. The proposed algorithms are implemented using Kalman filter and are applied to the in situ airborne GPS data. The performance of the proposed algorithms is validated by analyzing and comparing the results with those from reference values. The results show that reliable and comparable solutions in both position and kinematic acceleration levels can be obtained using the proposed algorithms.

No MeSH data available.


(a) Positions; (b) velocities; (c) accelerations.
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sensors-15-16895-f003: (a) Positions; (b) velocities; (c) accelerations.

Mentions: Figure 3a–c present the estimated positions, velocities and accelerations of the aircraft with respect to time, respectively. The estimated states are the results from forward-backward smoothing.


Medium to Long Range Kinematic GPS Positioning with Position-Velocity-Acceleration Model Using Multiple Reference Stations.

Hong CK, Park CH, Han JH, Kwon JH - Sensors (Basel) (2015)

(a) Positions; (b) velocities; (c) accelerations.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541913&req=5

sensors-15-16895-f003: (a) Positions; (b) velocities; (c) accelerations.
Mentions: Figure 3a–c present the estimated positions, velocities and accelerations of the aircraft with respect to time, respectively. The estimated states are the results from forward-backward smoothing.

Bottom Line: In order to obtain precise kinematic global positioning systems (GPS) in medium to large scale networks, the atmospheric effects from tropospheric and ionospheric delays need to be properly modeled and estimated.The performance of the proposed algorithms is validated by analyzing and comparing the results with those from reference values.The results show that reliable and comparable solutions in both position and kinematic acceleration levels can be obtained using the proposed algorithms.

View Article: PubMed Central - PubMed

Affiliation: Department of Geoinformatics Engineering, Kyungil University, 50 Gamasilgil, Kyeongsan, Gyeongbuk 712-701, Korea. ckhong@kiu.ac.kr.

ABSTRACT
In order to obtain precise kinematic global positioning systems (GPS) in medium to large scale networks, the atmospheric effects from tropospheric and ionospheric delays need to be properly modeled and estimated. It is also preferable to use multiple reference stations to improve the reliability of the solutions. In this study, GPS kinematic positioning algorithms are developed for the medium to large-scale network based on the position-velocity-acceleration model. Hence, the algorithm can perform even in cases where the near-constant velocity assumption does not hold. In addition, the estimated kinematic accelerations can be used for the airborne gravimetry. The proposed algorithms are implemented using Kalman filter and are applied to the in situ airborne GPS data. The performance of the proposed algorithms is validated by analyzing and comparing the results with those from reference values. The results show that reliable and comparable solutions in both position and kinematic acceleration levels can be obtained using the proposed algorithms.

No MeSH data available.