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Sensors 2017, 17(4), 887; doi:10.3390/s17040887

Multi-Mode Estimation for Small Fixed Wing Unmanned Aerial Vehicle Localization Based on a Linear Matrix Inequality Approach

1
School of Automation Science and Electrical Engineering, Beihang University, 37 Xueyuan Road, Haidian District, Beijing 100191, China
2
School of Control and Automation, MTC, Al-Khalifa Al-Maamoon Street Kobry Elkobbah, Cairo 11331, Egypt
*
Author to whom correspondence should be addressed.
Academic Editor: Felipe Gonzalez Toro
Received: 21 February 2017 / Revised: 28 March 2017 / Accepted: 14 April 2017 / Published: 18 April 2017
(This article belongs to the Section Remote Sensors)
View Full-Text   |   Download PDF [3447 KB, uploaded 18 April 2017]   |  

Abstract

Information fusion from multiple sensors ensures the accuracy and robustness of a navigation system, especially in the absence of global positioning system (GPS) data which gets degraded in many cases. A way to deal with multi-mode estimation for a small fixed wing unmanned aerial vehicle (UAV) localization framework is proposed, which depends on utilizing a Luenberger observer-based linear matrix inequality (LMI) approach. The proposed estimation technique relies on the interaction between multiple measurement modes and a continuous observer. The state estimation is performed in a switching environment between multiple active sensors to exploit the available information as much as possible, especially in GPS-denied environments. Luenberger observer-based projection is implemented as a continuous observer to optimize the estimation performance. The observer gain might be chosen by solving a Lyapunov equation by means of a LMI algorithm. Convergence is achieved by utilizing the linear matrix inequality (LMI), based on Lyapunov stability which keeps the dynamic estimation error bounded by selecting the observer gain matrix (L). Simulation results are presented for a small UAV fixed wing localization problem. The results obtained using the proposed approach are compared with a single mode Extended Kalman Filter (EKF). Simulation results are presented to demonstrate the viability of the proposed strategy. View Full-Text
Keywords: integrated navigation system; multi-mode estimation; sensor data fusion; Luenberger observer; small UAV localization integrated navigation system; multi-mode estimation; sensor data fusion; Luenberger observer; small UAV localization
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Elzoghby, M.; Li, F.; Arafa, I.I.; Arif, U. Multi-Mode Estimation for Small Fixed Wing Unmanned Aerial Vehicle Localization Based on a Linear Matrix Inequality Approach. Sensors 2017, 17, 887.

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