Improved robust finite-horizon Kalman filtering for uncertain networked time-varying systems

A novel robust finite-horizon Kalman filter is presented for networked linear time-varying systems with norm-bounded parameter uncertainty whether, or not, the data packets in the network are time-stamped. Measured data loss and latency in the communication link are both described by a Bernoulli distributed random sequence. Then, a two-stage recursive structure is employed for the robust Kalman filter. The filter parameters are determined such that the covariance of the estimation error does not exceed the prescribed upper bound. New augmented state-space model is employed to derive a procedure for computation of the filter parameters. The main novelty of the paper is to use the measurement reorganization technique for the robust Kalman filter design where the observation dropout and delay are both modeled by a stochastic process. Finally, the simulation results confirm the outperformance of the proposed robust Kalman filter compared to the rival methods in the literature.