Journal of Systems Engineering and Electronics ›› 2009, Vol. 31 ›› Issue (01): 200-203.

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Choice of cost functions in optimized Kalman filters

WANG Jian-wen1, SHUI Hai-tao1, MA Hong-xu1, LI Xun1, LIU Shu-tian2   

  1. 1. Dept. of Automatic Control, National Univ. of Defense Technology, Changsha 410073, China;
    2. Dept. of Teaching and Research, 91065 Unit, the PLA, Huludao 125001, China
  • Received:2007-06-22 Revised:2008-07-09 Online:2009-01-25 Published:2010-01-03

Abstract: The choice of cost functions in optimized Kalman filters(OKF) is thoroughly analyzed.Two cost functions suitable to OKFs are designed.It is proved that these cost functions are optimal,that is,when the cost function is minimal,the estimated state Hkxk|k-1⌒* is(or in probability) an optimal estimator for Hkxk in OKFs.Then,these cost functions are applied in a multiple model adaptive Kalman filter(MM-AKF),thus an optimized multiple model adaptive Kalman filter(OMM-AKF) is designed.The OMM-AKF can optimize the weights of state estimations,thus a superior state estimation can be obtained.Finally,the findings in this paper are verified by some simulations.

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