A low-complexity suboptimal filter for continuous–discrete linear systems with parametric uncertainties

We present a novel suboptimal filtering algorithm addressing estimation problems that arise in mixed continuous–discrete linear time-varying systems with stochastic parametric uncertainties. The suboptimal state estimate is formed by summing of local Kalman estimates with weights depending only on time instants tktk. In contrast to optimal weights, the suboptimal weights do not depend on current measurements, and thus the proposed filter is of a low-complexity and it can easily be implemented in real-time. High accuracy and efficiency of the suboptimal filter are demonstrated on the damper harmonic oscillator motion and the vehicle motion constrained to a plane.