A Probability-Dependent Optimal Kalman Predictor for Networked Systems Based on SCP

The optimal Kalman prediction algorithm relying on the communication probability is investigated for the networked linear discrete time-invariant system with stochastic communication protocol (SCP) and correlated noises. To mitigate data transmission conflicts and losses caused by the limited network bandwidth. Using SCP scheduling ensures that one measurement component is only sent from the sensor side to the predictor side in each communication. In situations where the predictor is unaware of the received measurement component but knows the communication probability of each component, an optimal recursive Kalman predictor relying on the transmission probability of each component is derived by applying the projection theory. Stability and steady-state properties are examined, and a condition sufficient to establish the presence of a steady-state predictor is yielded. Finally, the feasibility of the proposed algorithm is verified by a simulation example.