Multi-sensor networked state estimation with delayed and irregularly-spaced observations

The performance of a continuous-discrete Kalman filter using multi-sensor observations with irregular sampling patterns and/or delay in the measurement path is analyzed in terms of the associated error-covariance matrix. Such irregularities occur in geographically-distributed systems (such as the electric power grid) when observations are transmitted to an estimation/control center via an unreliable communication link. We extend here our earlier results on lower and upper bounds for the average error covariance to include the effects of communication delay and a more general class of sensor sampling irregularities.