@inproceedings{2278b967e4c34dac80f036a9759eeca1,
title = "Reliable eyes pose measurement for robotic bionic eyes with MEMS gyroscope and AKF filter",
abstract = "In order to obtain the precise pose information of the robotic bionic eyes, the MEMS gyroscope is used for the measurement. Considering that the output of the MEMS gyroscope contains random noise, it is necessary to reduce the measurement error by noise reduction. According to the mathematical characteristics of the original output signal, the autoregressive (AR) model is established, and an adaptive Kalman filter(AKF) is designed to process the data. The results show that this method effectively reduces the random drift of the MEMS gyroscope.",
keywords = "AKF, AR model, MEMS gyroscope, random drift",
author = "Guilin Liu and Owais, {Hafiz Muhammad} and Taoran Zhang and Shaowen Fu and Ye Tian and Xiaopeng Chen",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Cyborg and Bionic Systems, CBS 2017 ; Conference date: 17-10-2017 Through 19-10-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/CBS.2017.8266072",
language = "English",
series = "2017 IEEE International Conference on Cyborg and Bionic Systems, CBS 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "83--86",
booktitle = "2017 IEEE International Conference on Cyborg and Bionic Systems, CBS 2017",
address = "United States",
}