Optimal filtering with unknown inputs and reduced-order Kalman filter

This paper presents a new reduced-order Kalman filter for discrete-time dynamic stochastic linear systems to estimate a part of the state when all the measurements are affected by noises. The noninteresting part of the state is treated as an unknown input not constrained to evolve in accordance with a dynamic equation.