Kalman Filter for Systems with Communication Delay

This paper deals with estimating the process state where measurements are obtained via wireless networks. Transmission between the sensor and the estimator may be subject to random delays and/or data losses. A straightforward approach to address this problem is using a time-varying Kalman filter (KF) for the plant augmented by a delay model, which is computationally extensive. Research effort has recently focused on low complexity approximations of these filters. We revisit the time varying KF and, by exploiting the structure of the augmented process model, propose a new algorithm, which is computationally less extensive than the standard one. Moreover, interesting properties of the variable-delay estimators were obtained that are of independent interest.