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Sensors 2017, 17(5), 987; doi:10.3390/s17050987

Sensor Fusion Based on an Integrated Neural Network and Probability Density Function (PDF) Dual Kalman Filter for On-Line Estimation of Vehicle Parameters and States

1
Mechanical Engineering Department, Research Institute of Vehicle Safety, Universidad Carlos III de Madrid, Avda. de la Universidad 30, 28911 Madrid, Spain
2
Mechanical Engineering-Engineering Mechanics Department, Michigan Tech University, 1400 Townsend Drive, Houghton, MI 49931, USA
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Simon X. Yang
Received: 27 February 2017 / Revised: 20 April 2017 / Accepted: 24 April 2017 / Published: 29 April 2017
(This article belongs to the Special Issue Sensors for Transportation)
View Full-Text   |   Download PDF [15542 KB, uploaded 29 April 2017]   |  

Abstract

Vehicles with a high center of gravity (COG), such as light trucks and heavy vehicles, are prone to rollover. This kind of accident causes nearly 33 % of all deaths from passenger vehicle crashes. Nowadays, these vehicles are incorporating roll stability control (RSC) systems to improve their safety. Most of the RSC systems require the vehicle roll angle as a known input variable to predict the lateral load transfer. The vehicle roll angle can be directly measured by a dual antenna global positioning system (GPS), but it is expensive. For this reason, it is important to estimate the vehicle roll angle from sensors installed onboard in current vehicles. On the other hand, the knowledge of the vehicle’s parameters values is essential to obtain an accurate vehicle response. Some of vehicle parameters cannot be easily obtained and they can vary over time. In this paper, an algorithm for the simultaneous on-line estimation of vehicle’s roll angle and parameters is proposed. This algorithm uses a probability density function (PDF)-based truncation method in combination with a dual Kalman filter (DKF), to guarantee that both vehicle’s states and parameters are within bounds that have a physical meaning, using the information obtained from sensors mounted on vehicles. Experimental results show the effectiveness of the proposed algorithm. View Full-Text
Keywords: vehicle dynamics; dual Kalman filter; probability density function (PDF) truncation; state estimation; parameter estimation; vehicle roll angle; sensor fusion vehicle dynamics; dual Kalman filter; probability density function (PDF) truncation; state estimation; parameter estimation; vehicle roll angle; sensor fusion
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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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Vargas-Melendez, L.; Boada, B.L.; Boada, M.J.L.; Gauchia, A.; Diaz, V. Sensor Fusion Based on an Integrated Neural Network and Probability Density Function (PDF) Dual Kalman Filter for On-Line Estimation of Vehicle Parameters and States. Sensors 2017, 17, 987.

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