A unified filter for simultaneous input and state estimation of linear discrete-time stochastic systems

• Sze Zheng Yong ^[1] ; Minghui Zhu ^[2] ; Emilio Frazzoli ^[1]
  1. [1] Massachusetts Institute of Technology

     Massachusetts Institute of Technology

     City of Cambridge, Estados Unidos

     
  2. [2] Pennsylvania State University,USA
• Localización: Automatica: A journal of IFAC the International Federation of Automatic Control, ISSN 0005-1098, Vol. 63, 2016, págs. 321-329
• Idioma: inglés
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• Resumen

  • In this paper, we present a unified optimal and exponentially stable filter for linear discrete-time stochastic systems that simultaneously estimates the states and unknown inputs in an unbiased minimum-variance sense, without making any assumptions on the direct feedthrough matrix. We also provide the connection between the stability of the estimator and a system property known as strong detectability, and discuss the global optimality of the proposed filter. Finally, an illustrative example is given to demonstrate the performance of the unified unbiased minimum-variance filter.