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Least-Mean-Square Receding Horizon Estimation

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  • Bokyu Kwon
  • Soohee Han

Abstract

We propose a least-mean-square (LMS) receding horizon (RH) estimator for state estimation. The proposed LMS RH estimator is obtained from the conditional expectation of the estimated state given a finite number of inputs and outputs over the recent finite horizon. Any a priori state information is not required, and existing artificial constraints for easy derivation are not imposed. For a general stochastic discrete-time state space model with both system and measurement noise, the LMS RH estimator is explicitly represented in a closed form. For numerical reliability, the iterative form is presented with forward and backward computations. It is shown through a numerical example that the proposed LMS RH estimator has better robust performance than conventional Kalman estimators when uncertainties exist.

Suggested Citation

  • Bokyu Kwon & Soohee Han, 2012. "Least-Mean-Square Receding Horizon Estimation," Mathematical Problems in Engineering, Hindawi, vol. 2012, pages 1-19, March.
    • Handle: RePEc:hin:jnlmpe:631759
    DOI: 10.1155/2012/631759
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