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Sensors 2015, 15(6), 14569-14590; doi:10.3390/s150614569

On-Board Event-Based State Estimation for Trajectory Approaching and Tracking of a Vehicle

Department of Electronics, University of Alcalá. Polytechnic School, Campus Universitario, Alcalá de Henares 28871, Spain
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Author to whom correspondence should be addressed.
Academic Editor: Albert M. K. Cheng
Received: 1 April 2015 / Revised: 9 June 2015 / Accepted: 17 June 2015 / Published: 19 June 2015
(This article belongs to the Special Issue Cyber-Physical Systems)
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Abstract

For the problem of pose estimation of an autonomous vehicle using networked external sensors, the processing capacity and battery consumption of these sensors, as well as the communication channel load should be optimized. Here, we report an event-based state estimator (EBSE) consisting of an unscented Kalman filter that uses a triggering mechanism based on the estimation error covariance matrix to request measurements from the external sensors. This EBSE generates the events of the estimator module on-board the vehicle and, thus, allows the sensors to remain in stand-by mode until an event is generated. The proposed algorithm requests a measurement every time the estimation distance root mean squared error (DRMS) value, obtained from the estimator’s covariance matrix, exceeds a threshold value. This triggering threshold can be adapted to the vehicle’s working conditions rendering the estimator even more efficient. An example of the use of the proposed EBSE is given, where the autonomous vehicle must approach and follow a reference trajectory. By making the threshold a function of the distance to the reference location, the estimator can halve the use of the sensors with a negligible deterioration in the performance of the approaching maneuver. View Full-Text
Keywords: event-based state estimation; indoor localization; non-linear filtering, trajectory tracking event-based state estimation; indoor localization; non-linear filtering, trajectory tracking
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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. (CC BY 4.0).

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MDPI and ACS Style

Martínez-Rey, M.; Espinosa, F.; Gardel, A.; Santos, C. On-Board Event-Based State Estimation for Trajectory Approaching and Tracking of a Vehicle. Sensors 2015, 15, 14569-14590.

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