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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
