@inproceedings{82f52cf555ee4220b59092296267fdea,
title = "Attitude estimation for UAV with low-cost IMU/ADS based on adaptive-gain complementary filter",
abstract = "This paper describes a robust and practical complementary filter (CF) algorithm for unmanned aerial vehicle (UAV) attitude estimation with low-cost inertial measurement unit (IMU) and embedded air data system (ADS). Utilizing a fuzzy logical system, the UAV dynamic modes including different accelerations and turns can be attained. Based on the compensation of acceleration and centrifugal forces in turns using ADS information, the gains of complementary filter adapts to the dynamic modes to yield robust performances. The simulation and experimental results show that the proposed adaptive-gain complementary filter approach can obtain robust and accurate attitude estimation even when the UAV is subject to strong acceleration or in turn mode.",
keywords = "Adaptive-gain complementary filter, Attitude estimation, Fuzzy logic system, UAV",
author = "Lingling Wang and Li Fu and Xiaoguang Hu and Guofeng Zhang",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 13th International Symposium on Neural Networks, ISNN 2016 ; Conference date: 06-07-2016 Through 08-07-2016",
year = "2016",
doi = "10.1007/978-3-319-40663-3\_40",
language = "英语",
isbn = "9783319406626",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "346--355",
editor = "Long Cheng and Qingshan Liu and Andrey Ronzhin",
booktitle = "Advances in Neural Networks - 13th International Symposium on Neural Networks, ISNN 2016, Proceedings",
address = "德国",
}