Abstract
This paper introduces a new Gaussian filter for nonlinear systems plagued by correlated noises and unknown measurement loss.It tackles the issue of correlated noises by employing a Gaussian approximation recursive filter (GASF).To approximate the nonlinear aspects, it utilizes the Unscented Transformation (UT).Furthermore, it estimates measurement loss using the Maximum a Posteriori (MAP) criterion.Through simulations, this paper demonstrates the effectiveness of the proposed algorithm under measurement loss and correlated noises.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of 2024 IEEE International Conference on Unmanned Systems, ICUS 2024 |
| Editors | Rong Song |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 26-31 |
| Number of pages | 6 |
| ISBN (Electronic) | 9798350384185 |
| DOIs | |
| Publication status | Published - 2024 |
| Event | 2024 IEEE International Conference on Unmanned Systems, ICUS 2024 - Nanjing, China Duration: 18 Oct 2024 → 20 Oct 2024 |
Publication series
| Name | Proceedings of 2024 IEEE International Conference on Unmanned Systems, ICUS 2024 |
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Conference
| Conference | 2024 IEEE International Conference on Unmanned Systems, ICUS 2024 |
|---|---|
| Country/Territory | China |
| City | Nanjing |
| Period | 18/10/24 → 20/10/24 |
Keywords
- MAP estimation
- Nonlinear system
- correlated noises
- unknown measurement loss
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Liu, C., Zheng, D., Ji, Q., Yan, J., & Jiang, J. (2024). A Gaussian Filter for Nonlinear Systems With Correlated Noises and Unknown Sensor Measurement Loss. In R. Song (Ed.), Proceedings of 2024 IEEE International Conference on Unmanned Systems, ICUS 2024 (pp. 26-31). (Proceedings of 2024 IEEE International Conference on Unmanned Systems, ICUS 2024). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICUS61736.2024.10840139