Environmental Features Assisted Satellite Navigation
A special issue of Geomatics (ISSN 2673-7418).
Deadline for manuscript submissions: 23 January 2026 | Viewed by 4
Special Issue Editor
Interests: GNSS positioning in challenging environments; 3D-mapping-aided GNSS; collaborative positioning; signal propagation modelling; multi-sensor integration; machine-learning-aided positioning; indoor positioning
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Special Issue Information
Dear Colleagues,
The global navigation satellite system (GNSS), which provides accurate position information at an economic cost, has become vital for various civil applications, especially for location-based services. However, GNSS performance can be easily degraded by the surrounding environmental objects, such as, for example, the buildings in urban areas. Environmental objects may obstruct, reflect, or diffract GNSS signals, introducing availability reductions, signal attenuation, multipath effects, and non-line-of-sight receptions, resulting in significant degradation of GNSS performance.
The traditional solution is to improve the robustness of estimation algorithms against degraded measurements (as outliers), whereas the available information is usually limited to perform adequate error mitigation. Since GNSS measurement errors are introduced by environmental objects, it is reasonable to obtain the surrounding environment’s information to improve the performance of GNSS. Thus, recent studies tend to explore the opportunity of employing environmental information (e.g., extracts from additional sensors, external databases, or even measurement itself via deep learning), which is correlated with the measurement degradations, to assist GNSS error detection, mitigation, or even correction.
The goal of this Special Issue on “Environmental Features Assisted Satellite Navigation” is to collect papers (original research articles and review papers) to give insights about associating environmental features from various approaches (such as mapping, spatial data, external sensor observations, deep learning networks, etc.) with GNSS degradations, thereby assisting adequate error detection, mitigation, or correction to improve GNSS positioning and navigation performance.
This Special Issue will welcome manuscripts that link themes that include, but are not limited to, the following:
- Environment-adaptive GNSS positioning;
- Integrating GNSS with environment-perception sensors;
- GNSS multipath or non-line-of-sight reception modeling based on mapping data;
- Vision or LiDAR feature-aided GNSS;
- AI-based context awareness for GNSS;
- Environment information aided GNSS fault detection and exclusion;
- GNSS signal propagation modeling in complex environments;
- Advanced GNSS signal processing based on environmental features;
- GNSS performance assessment or benchmarking in challenging environments.
We look forward to receiving your original research articles and reviews.
Dr. Guohao Zhang
Guest Editor
Manuscript Submission Information
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- global navigation satellite system
- environmental features
- adaptive navigation
- multipath effect
- multi-sensor integration
- mapping data
- context awareness
- deep learning
- signal propagation
- urban areas
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