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Sensors 2017, 17(10), 2243; doi:10.3390/s17102243

A Novel Online Data-Driven Algorithm for Detecting UAV Navigation Sensor Faults

1
College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
2
Centre for Transport Studies, Imperial College London, London SW7 2AZ, UK
*
Author to whom correspondence should be addressed.
Received: 23 July 2017 / Revised: 13 September 2017 / Accepted: 26 September 2017 / Published: 29 September 2017
(This article belongs to the Section Remote Sensors)
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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. View Full-Text
Keywords: online; data-driven; navigation sensor fault detection; adaptive neuron fuzzy inference system online; data-driven; navigation sensor fault detection; adaptive neuron fuzzy inference system
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Sun, R.; Cheng, Q.; Wang, G.; Ochieng, W.Y. A Novel Online Data-Driven Algorithm for Detecting UAV Navigation Sensor Faults. Sensors 2017, 17, 2243.

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