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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 515787, 14 pages
http://dx.doi.org/10.1155/2015/515787
Research Article

A Study on Maneuvering Obstacle Motion State Estimation for Intelligent Vehicle Using Adaptive Kalman Filter Based on Current Statistical Model

1State Key Laboratory of Automobile Simulation and Control, Jilin University, Changchun 130022, China
2Ford Motor Research & Engineering (Nanjing) Co., Ltd., Nanjing 210000, China

Received 2 May 2015; Revised 7 August 2015; Accepted 9 August 2015

Academic Editor: Raffaele Solimene

Copyright © 2015 Bao Han et al. 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.

Abstract

The obstacle motion state estimation is an essential task in intelligent vehicle. The ASCL group has developed such a system that uses a radar and GPS/INS. When running on the road, the acceleration of the vehicle is always changing, so it is hard for constant velocity (CV) model and constant acceleration (CA) model to describe the motion state of the vehicle. This paper introduced Current Statistical (CS) model from military field, which uses the modified Rayleigh distribution to describe acceleration. The adaptive Kalman filter based on CS model was used to estimate the motion state of the target. We conducted simulation experiments and real vehicle tests, and the results showed that the estimation of position, velocity, and acceleration can be precise.