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

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  • Bao Han
  • Guan Xin
  • Jia Xin
  • Liu Fan

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.

Suggested Citation

  • Bao Han & Guan Xin & Jia Xin & Liu Fan, 2015. "A Study on Maneuvering Obstacle Motion State Estimation for Intelligent Vehicle Using Adaptive Kalman Filter Based on Current Statistical Model," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-14, August.
    • Handle: RePEc:hin:jnlmpe:515787
    DOI: 10.1155/2015/515787
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