Outlier-Resistant Initial Alignment of DVL-Aided SINS Using Mahalanobis Distance
Yidong Shen, Li Luo, Guoqing Wang, Tao Liu, Lin Luo, Jiaxi Guo, Shuangshuang Wang · 2025
Read Outlier-Resistant Initial Alignment of DVL-Aided SINS Using Mahalanobis Distance by Yidong Shen, Li Luo, Guoqing Wang, Tao Liu, Lin Luo, Jiaxi Guo, Shuangshuang Wang (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12737279.txt.
Description
Yidong Shen and co-authors harden in 2025 the startup routine of an underwater navigation system against bad data. A strapdown inertial system knows where it points only after an initial alignment, and a Doppler velocity log helps by measuring motion through water. Underwater, that help arrives contaminated: currents and echoes inject outlier readings that can tilt the whole alignment.
Their method watches the stream of observation vectors for anomalies using a smarter distance measure, one that accounts for how the measurements naturally vary together. Suspect readings are not deleted but downweighted, through an adaptive factor folded into the observation math, so a few wild points cannot steer the result. Simulations and real experiments both showed cleaner alignment than existing methods when outliers were present. For vehicles that navigate blind under the sea, a trustworthy first fix is the difference between a mission and a search party.
Catalog record
| CREATOR | Yidong Shen, Li Luo, Guoqing Wang, Tao Liu, Lin Luo, Jiaxi Guo, Shuangshuang Wang |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 30 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |