An Adaptability Analysis Method Based on the Quantification of Terrain Matching Performance
As an important intersection of geoscience, remote sensing, and navigation, terrain-aided inertial navigation leverages detailed terrain information for precise positioning. In terrain-aided inertial navigation system (INS), adaptability analysis serves as the foundation for adaptation area selection and trajectory planning, playing a crucial role in determining terrain matching accuracy. However, traditional adaptability analysis methods are often disconnected from the practical requirements of terrain matching. They fail to comprehensively account for factors influencing positioning accuracy, resulting in a gap between adaptability analysis and actual terrain matching performance. To bridge this gap, this article proposes a novel adaptability analysis method based on the quantification of terrain matching performance, which provides a reliable means of quantifying the terrain matching performance. First, the sources of terrain matching errors are examined and classified into two types: local error and similarity error. Then, theoretical derivations provide an analytical expression for local error, while similarity error is expressed as probability. Finally, these two errors are combined into a scalar adaptability index called adaptability error, that effectively reflects terrain matching accuracy. The proposed adaptability error incorporates key influencing factors, including sensor errors, digital terrain map (DTM) errors, and terrain similarity. Simulations and experiments validate its effectiveness, demonstrating a strong correlation between adaptability error and actual matching error.
Authors
- Wenzhe Zhang (ORCID: https://orcid.org/0000-0002-0273-7239)
- Zhaonan Jiao
- Shengwu Zhao (ORCID: https://orcid.org/0000-0003-3459-3062)
- Zhihong Deng (ORCID: https://orcid.org/0000-0003-0226-0626)
Institutions
- Beijing Institute of Technology (CN)
Publication Details
- Journal
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1109/tgrs.2025.3598270
- Citations
- 1
- Primary Topic
- Remote Sensing and Land Use
- Type
- article
- Field-Weighted Citation Impact
- 1.30
Funders
- National Natural Science Foundation of China