A novel Kalman filter for vehicle localization subject to measurement outliers
In this technical note, we investigate a novel Kalman Filter for vehicle localization with explicit mechanisms to address measurement outliers. Specifically, by applying the saturation mechanism to the innovation, the state update phase is corrected under the adaptive adjustment of the saturation bounds, thus restraining the influence of outliers. As for modeling error, an improved Sage–Husa adaptive filtering strategy is introduced to dynamically update the statistical properties of the measurement noise.
Authors
- E. Wang (ORCID: https://orcid.org/0000-0003-2831-3108)
- Jiageng Liu (ORCID: https://orcid.org/0000-0002-7104-4399)
- Meihong Zhao (ORCID: https://orcid.org/0000-0003-3069-2880)
Institutions
- Shenyang Ligong University (CN)
- Liaoning Engineering Vocational College (CN)
Publication Details
- Journal
- Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering
- Published
- 2026-10-13
- DOI
- https://doi.org/10.1177/09544070261496224
- Primary Topic
- Target Tracking and Data Fusion in Sensor Networks
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Liaoning Province