An autoregressive (AR) model based stochastic unknown input realization and filtering technique Conference Paper uri icon


  • 2015 American Automatic Control Council. This paper studies the state estimation problem of linear discrete-time systems with stochastic unknown inputs. The unknown input is a wide-sense stationary process while no other prior information needs to be known. We propose an autoregressive (AR) model based unknown input realization technique which allows us to recover the input statistics from the output data by solving an appropriate least squares problem, then fit an AR model to the recovered input statistics and construct an innovations model of the unknown inputs using the eigensystem realization algorithm (ERA). An augmented state system is constructed and the standard Kalman filter is applied for state estimation. A reduced order model (ROM) filter is also introduced to reduce the computational cost of the Kalman filter. One numerical example is given to illustrate the procedure.

name of conference

  • 2015 American Control Conference (ACC)

published proceedings


author list (cited authors)

  • Yu, D., & Chakravorty, S.

citation count

  • 2

complete list of authors

  • Yu, Dan||Chakravorty, Suman

publication date

  • January 2015