Results 221 to 230 of about 46,320 (319)
Abstract Whillans Ice Plain (WIP), a region of West Antarctica flowing into the southern Ross Sea, lurches forward 0.2–0.6 m over 30–60 min once or twice per diurnal ocean tidal cycle. Combining 11 years (2008–2019) of 30 s or better resolution Global Navigation Satellite System (GNSS) data from past field campaigns is necessary to provide insight into
Z. S. Katz, M. R. Siegfried, L. Padman
wiley +1 more source
A Study on a High-Precision 3D Position Estimation Technique Using Only an IMU in a GNSS Shadow Zone. [PDF]
Ding Y, Kim Y, Kim H.
europepmc +1 more source
Abstract The intensified solar maximum of Cycle 25 has heightened space weather risks to Global Navigation Satellite Systems (GNSS), where geomagnetic storms critically challenge Positioning, Navigation, and Timing (PNT) availability. Current wide‐area ionospheric models may fail to resolve extreme latitudinal Total Electron Content (TEC) gradients ...
Tong Liu +5 more
wiley +1 more source
Development of an active counter unmanned aerial system with integrated autonomous mobile robot for inspection and defense. [PDF]
Li S, Fan K, Cai W, Wang L, Fan A.
europepmc +1 more source
Prediction of Multipath Interference for Static GNSS Applications
Yedukondalu Kamatham
openalex +2 more sources
Abstract Accurate modeling of traveling ionospheric disturbances (TIDs) is essential for characterizing their spatiotemporal variations and mitigating their effects on Global Navigation Satellite System (GNSS) precise positioning. This study develops a regional medium‐scale TID (MSTID) propagation model using BeiDou geostationary orbit (GEO) total ...
Dengkui Mei +5 more
wiley +1 more source
Neural Networks for Estimating Attitude, Line of Sight, and GNSS Ambiguity Through Onboard Sensor Fusion. [PDF]
de Celis R, Cadarso L.
europepmc +1 more source
Navigating Latency Hurdles: An In-Depth Examination of a Cloud-Powered GNSS Real-Time Positioning Application on Mobile Devices [PDF]
Jorge Hernández Olcina +2 more
openalex +1 more source
Global Ionospheric Slab Thickness Prediction Model Using XGBoost and Ensemble Learning
Abstract The ionospheric equivalent slab thickness is a key parameter for understanding the plasma distribution in the ionosphere, with direct relevance to satellite navigation, communication, and skywave over‐the‐horizon radar. However, traditional prediction methods often suffer from regional biases, limiting their global applicability.
C. Han +8 more
wiley +1 more source

