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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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For more interesting games or algorithms, check out “Mapoet’s GitHub” or check out the starred projects…
Rubiks Cube With Custom order implemented through OpenGL.
Global Ionospheric Assimilation Model: Data, Method, Errors, and the DCBs of receivers and transmitters.
Multi-body motion visualization driven by the EIH equation is implemented by OpenGL.
Assisting teachers in completing reports and experiments.
In the Shanghai Youth Science Innovation Practice, I assisted Li Ligang, a researcher to complete the project Light Speed Measurement Based on Io eclipse.
Retrieval Processing Technique For LEO-LEO Radio Occultation Atmospheric Data and Error Sources Analysis
Published in RemoteSensingScience, 2016
By statistics of the retrieval temperature and humidity, we found: the existing satellite clock stability and the LEO satellite orbit determination accuracy meet the needs of the occultation inversion.
Recommended citation: Fu N. F., Guo P., Wu M.J., etal. Retrieval Processing Technique For LEO-LEO Radio Occultation Atmospheric Data and Error Sources Analysis [J]. RemoteSensingScience, 2016, 4(2): 51 - 64. http://www.ivypub.org/RSS/download/33584.shtml
The Two-Parts Step-by-Step Ionospheric Assimilation Based on Ground-Based/Spaceborne Observations and Its Verification.
Published in Remote Sens., 2019
This study introduced a Kalman filtering assimilation model that considers the DCB errors of GPS/LEO satellites and GNSS stations. The assimilation results and reliability were verified by various types of data, such as ionMap, ionosonde, ISR, and the EDP of ionPrf from COSMIC.
Recommended citation: Fu, N.; Guo, P.; Wu, M.; Huang, Y.; Hu, X.; Hong, Z. The Two-Parts Step-by-Step Ionospheric Assimilation Based on Ground-Based/Spaceborne Observations and Its Verification. Remote Sens. 2019, 11, 1172. https://www.mdpi.com/2072-4292/11/10/1172
Published in Advances in Space Research, 2019
With the Nequick model as the initial field of the Kalman-filter(KF) algorithm, the global ionospheric density field was constructed by KF algorithm, and the subsequent works were processed.
Recommended citation: Fu, N.; Guo, P.; Wu, M.; Huang, Y.; Hu, X.; Hong, Z. Simulating and Assimilating Ionospheric Observations with Multisources. Advances in Space Research 2019.
Error Sources of Imaging the Ionosphere and Assimilating the Ionospheric Observations from Multi-constellation/system.
In this paper, using the International Reference Ionosphere model as the real field, ionospheric observational data from ground-based and spaceborne systems on Jan. 1st, 2008 were simulated. With the Nequick model as the background field,the global ionospheric density field was constructed by the Kalman-filter algorithm, and the subsequent work was processed. Various errors and influences in the ionospheric inversion, especially easily overlooked errors, were analyzed,and corresponding improvement methods were proposed and verified.The effects of constellation/system observations on the observation quality and spatial distribution configuration of the ionospheric inversion were analyzed.Then,the characteristics of the Kalman filter assimilation algorithm and Abel inversion algorithm were compared.
Through assimulation local areas in space were over corrected due to the effects of the top layer; these overcorrections should be reduced before processing,and the time/grid correction can effectively and steadily improve the assimilation accuracy. The introduction of COSMIC occultation data can effectively fill the void in the ocean data and increase the ionospheric observational density and horizontal accuracy significantly, and it was showed that the Kalman filter assimilation algorithm has higher accuracy,especially in ionospheric peak altitude.