Abstract
The use of Unmanned Aerial Vehicles (UAVs) has increased significantly in recent years. On-board integrated navigation sensors are a key component of UAVs’ flight control systems and are essential for flight safety. In order to ensure flight safety, timely and effective navigation sensor fault detection capability is required. In this paper, a novel data-driven Adaptive Neuron Fuzzy Inference System (ANFIS)-based approach is presented for the detection of on-board navigation sensor faults in UAVs. Contrary to the classic UAV sensor fault detection algorithms, based on predefined or modelled faults, the proposed algorithm combines an online data training mechanism with the ANFIS-based decision system. The main advantages of this algorithm are that it allows real-time model-free residual analysis from Kalman Filter (KF) estimates and the ANFIS to build a reliable fault detection system. In addition, it allows fast and accurate detection of faults, which makes it suitable for real-time applications. Experimental results have demonstrated the effectiveness of the proposed fault detection method in terms of accuracy and misdetection rate.
Highlights
Unmanned Aerial Vehicle (UAV) applications have been increasing in recent years, including surveillance, reconnaissance, search/destroy missions, aerial photography, and disaster monitoring [1].with the rapid development of these applications, some of which are safety critical, Unmanned Aerial Vehicles (UAVs) flight safety has become a critical issue
Misdetection rate, false alarm rate, and computation time, the best performance in terms of accuracy (97.7%), false alarm (13%), and time consumption (11 s) were all achieved at Training Condition (TC) = 100
While the accuracy steadily increased and the false alarm rate decreased as the TC value was increased from 1–100, increasing the value of TC to 150 results in the performance deteriorating slightly compared to the performance at TC = 100
Summary
Unmanned Aerial Vehicle (UAV) applications have been increasing in recent years, including surveillance, reconnaissance, search/destroy missions, aerial photography, and disaster monitoring [1]. Fuzzy Inference System (ANFIS)-based algorithm for the detection of UAV navigation sensor faults This method integrates the online data-driven detection cycle with the KF residuals and an ANFIS-based fault detector. The latest features of all types of faults (point, contextual, and collective) present in the database are used for the extraction of fault judgement rules; (3) Robust fault detection decision making and more detailed analysis of the algorithm—the proposed KF residual with the ANFIS-based decision-making method is more robust compared to the simple Mahalanobis distance-based detector in [4,12,13], and faster than the Particle Filter (PF)- and Fuzzy Inference System (FIS)-based algorithm in [11].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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.
