scispace - formally typeset
J

Jingnan Liu

Researcher at Wuhan University

Publications -  114
Citations -  3267

Jingnan Liu is an academic researcher from Wuhan University. The author has contributed to research in topics: GNSS applications & Precise Point Positioning. The author has an hindex of 27, co-authored 96 publications receiving 2589 citations. Previous affiliations of Jingnan Liu include Xi'an University of Science and Technology.

Papers
More filters
Journal ArticleDOI

Precise relative positioning using real tracking data from COMPASS GEO and IGSO satellites

TL;DR: A quality analysis is presented using 1-week COMPASS measurements collected in Wuhan and the accuracy of GPS/COMPASS combination solutions is at least 20 % better than that of GPS alone.
Journal ArticleDOI

Precise orbit determination for quad-constellation satellites at Wuhan University: strategy, result validation, and comparison

TL;DR: In this paper, the authors analyzed the accuracy and stability of the quad-constellation products from MGEX Analysis Centers (ACs) for a common time period of 1 year (2014).
Journal ArticleDOI

Initial results of precise orbit and clock determination for COMPASS navigation satellite system

TL;DR: In this article, the precise orbit determination strategy of the COMPASS satellites is presented, and the obtained orbits are evaluated by analysis of post-fit residuals, orbit overlap comparison and SLR (satellite laser ranging) validation.
Journal ArticleDOI

Precise orbit determination of Beidou Satellites with precise positioning

TL;DR: Research in this paper verifies that, with support of ground reference station network, Beidou satellite navigation system can provide precise positioning from several decimeters to meters in the wide area and several centimeters in the regional area.
Journal ArticleDOI

Total least squares adjustment in partial errors-in-variables models: algorithm and statistical analysis

TL;DR: In this article, the weighted total least squares (TLS) method has been extended to a partial EIV model, in which not all the elements of the design matrix are random and the total number of unknowns in the normal equations has been significantly reduced.