Hindawi Publishing Corporation
Mathematical Problems in Engineering
Volume 2011, Article ID 174618, 20 pages
doi:10.1155/2011/174618
Research Article
Detection of Sensor Faults in Small Helicopter
UAVs Using Observer/Kalman Filter Identification
Guillermo Heredia
1
and Anibal Ollero
1, 2
1
Robotics, Vision and Control Group, University of Seville, Camino de los Descubrimientos s/n,
41092 Seville, Spain
2
Center for A dvanced Aerospace Technologies (CATEC), Aeropolis, Seville, Spain
Correspondence should be addressed to Guillermo Heredia, guiller@cartuja.us.es
Received 1 April 2011; Accepted 14 July 2011
Academic Editor: Horst Ecker
Copyright q 2011 G. Heredia and A. Ollero. This is an open access article distributed under
the Creative Commons Attribution License, which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.
Reliability is a critical issue in navigation of unmanned aerial vehicles UAVs since there is no
human pilot that c an react to any abnormal situation. Due to size and cost limitations, redun-
dant sensor schemes and aeronautical-grade navigation sensors used in large aircrafts cannot be
installed in small UAVs. Therefore, other approaches like analytical redundancy should be used
to detect faults in navigation sensors and increase reliability. This paper presents a sensor fault
detection and diagnosis system for small autonomous helicopters based on analytical redundancy.
Fault detection is accomplished by evaluating any significant change in the behaviour of the
vehicle with respect to the fault-free behaviour, which is estimated by using an observer. The
observer is obtained from input-output experimental data with the Observer/Kalman Filter
Identification OKID method. T he OKID method is able to identify the system and an observer
with properties similar to a Kalman filter, directly from input-output experimental data. Results
are similar to the Kalman filter, but, with the proposed method, there is no need to estimate neither
system matrices nor sensor and process noise covariance matrices. The system has been tested with
real helicopter flight data, and the results compared with other methods.
1. Introduction
Unmanned aerial vehicles UAVs are increasingly used in many applications in which
ground vehicles cannot access to the desired locations due to the characteristics of the terrain
and the presence of obstacles. In many cases, the use of aerial vehicles is the best way to ap-
proach the objective to get information or to deploy instrumentation.
Fixed wing UAVs, rotorcrafts, and airships with different characteristics have been pro-
posed and experimented see, e.g., 1. Helicopters have high manoeuvrability and hover-
ing ability. Then, they are well suited to agile target tracking tasks, as well as to inspection
