Kalman filtering for wireless networks systems with delayed-missing measurements

The paper is concerned with the Kalman filtering problem for linear discrete-time network systems with delayed-missing measurements. The Kalman filter and the corresponding covariance matrix become stochastic, since the measurements are partial (or part or all of the measurements are lost). The re-organized innovation analysis approach is proposed to deal with such a problem, and the calculation of the filter involves two Riccati equations associated with two normal Kalman filter of a delay-free system. The Riccati equations have the same dimension as the system.