Kalman filtering over a packet-delaying network: A probabilistic approach

In this paper, we consider Kalman filtering over a packet-delaying network. Given the probability distribution of the delay, we can characterize the filter performance via a probabilistic approach. We assume that the estimator maintains a buffer of length D so that at each time k, the estimator is able to retrieve all available data packets up to time k−D+1. Both the cases of sensor with and without necessary computation capability for filter updates are considered. When the sensor has no computation capability, for a given D, we give lower and upper bounds on the probability for which the estimation error covariance is within a prescribed bound. When the sensor has computation capability, we show that the previously derived lower and upper bounds are equal to each other. An approach for determining the minimum buffer length for a required performance in probability is given and an evaluation on the number of expected filter updates is provided. Examples are provided to demonstrate the theory developed in the paper