@inproceedings{ad03d1adfb694da0b343faaab1cfc0d9,
title = "A Fault-Tolerant Navigation Method for Multirotor UAVs Based on Federal Adaptive Kalman Filter",
abstract = "With the rapid development of multi-sensor data fusion technology, the integrated navigation system is becoming more and more complex, so the faulty of a single component will affect the whole navigation system. To improve the fault tolerance performance of integrated navigation system, a fault-tolerant integrated navigation algorithm combining Federal Kalman Filter (FKF) and Adaptive Kalman Filter (AKF) is proposed in this paper. Introducing adaptive factors into the structure of FKF can reduce the influence of sensor failures and modeling errors. The real flight data from multirotor unmanned aerial vehicles (UAVs) is used for simulation. And the result of simulation shows that the proposed federal adaptive fault-tolerant integrated navigation algorithm based on FKF and AKF can increase the filtering accuracy and reliability significantly.",
keywords = "Adaptive kalman filter, Fault-tolerant navigation, Federal kalman filter",
author = "Xiaoxiong Liu and Ju, {Yu Ting} and Gao, {Yan Zhao} and Li, {Chang Ze}",
note = "Publisher Copyright: {\textcopyright} 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; International Conference on Guidance, Navigation and Control, ICGNC 2020 ; Conference date: 23-10-2020 Through 25-10-2020",
year = "2022",
doi = "10.1007/978-981-15-8155-7_132",
language = "英语",
isbn = "9789811581540",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "1577--1588",
editor = "Liang Yan and Haibin Duan and Xiang Yu",
booktitle = "Advances in Guidance, Navigation and Control - Proceedings of 2020 International Conference on Guidance, Navigation and Control, ICGNC 2020",
}