@inproceedings{5a8511ff303d4f4396edab2b68f0ca84,
title = "MEMS Sensor Data Anomaly Detection for the UAV Flight Control Subsystem",
abstract = "MEMS sensor is being applied more and more in Unmanned Aerial Vehicle (UAV), especially for the flight control of UAV. To enhance MEMS sensor reliability, a data-driven model based on the combination of Kernel Principal Component Analysis (KPCA) and flight mode is proposed. The raw data of MEMS sensor are classified by the flight mode. Then, the training and testing data for KPCA to detect the target data are determined accordingly. False positive rate is utilized as metric to weight the performance of the anomaly detection, which can be adopted to measure the MEMS sensor reliability. The evaluation experiments are implemented based on the practical MEMS sensor data of UAV flight control subsystem. Experimental results demonstrate the effectiveness of the proposed model.",
keywords = "MEMS sensor, anomaly detection, reliability, unmanned aerial vehicle",
author = "Liansheng Liu and Mei Liu and Qing Guo and Datong Liu and Yu Peng",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 17th IEEE SENSORS Conference, SENSORS 2018 ; Conference date: 28-10-2018 Through 31-10-2018",
year = "2018",
month = dec,
day = "26",
doi = "10.1109/ICSENS.2018.8589748",
language = "英语",
series = "Proceedings of IEEE Sensors",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2018 IEEE SENSORS, SENSORS 2018 - Conference Proceedings",
address = "美国",
}