@inproceedings{650d160faf8248abafd05fe6954ccb8a,
title = "A Gaussian Filter for Nonlinear Systems With Correlated Noises and Unknown Sensor Measurement Loss",
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.",
keywords = "MAP estimation, Nonlinear system, correlated noises, unknown measurement loss",
author = "Congyi Liu and Dezhi Zheng and Qiutong Ji and Jun Yan and Jie Jiang",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 IEEE International Conference on Unmanned Systems, ICUS 2024 ; Conference date: 18-10-2024 Through 20-10-2024",
year = "2024",
doi = "10.1109/ICUS61736.2024.10840139",
language = "English",
series = "Proceedings of 2024 IEEE International Conference on Unmanned Systems, ICUS 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "26--31",
editor = "Rong Song",
booktitle = "Proceedings of 2024 IEEE International Conference on Unmanned Systems, ICUS 2024",
address = "United States",
}