Distributed fusion receding horizon filtering in linear stochastic systems

  • Authors:

  • Il Young Song;Du Yong Kim;Yong Hoon Kim;Suk Jae Lee;Vladimir Shin

  • Affiliations:

  • School of Information and Mechatronics, Gwangju Institute of Science and Technology, Gwangju, South Korea;School of Information and Mechatronics, Gwangju Institute of Science and Technology, Gwangju, South Korea;School of Information and Mechatronics, Gwangju Institute of Science and Technology, Gwangju, South Korea;Agency for Defense Development, Yuseong, Daejeon, South Korea;School of Information and Mechatronics, Gwangju Institute of Science and Technology, Gwangju, South Korea

  • Venue:
  • EURASIP Journal on Advances in Signal Processing
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

This paper presents a distributed receding horizon filtering algorithm for multisensor continuous-time linear stochastic systems. Distributed fusion with a weighted sum structure is applied to local receding horizon Kalman filters having different horizon lengths. The fusion estimate of the state of a dynamic system represents the optimal linear fusion by weighting matrices under the minimum mean square error criterion. The key contribution of this paper lies in the derivation of the differential equations for determining the error cross-covariances between the local receding horizon Kalman filters. The subsequent application of the proposed distributed filter to a linear dynamic system within a multisensor environment demonstrates its effectiveness.