Performance vs complexity trade-offs for Markovian networked jump estimators

This paper addresses the design of a state observer for networked systems with random delays and dropouts. The model of plant and network covers the cases of multiple sensors, out-of-sequence and buffered measurements. The measurement outcomes over a finite interval model the network measurement reception scenarios, which follow a Markov distribution. We present a tractable optimization problem to precalculate off-line a finite set of gains of jump observers. The proposed procedure allows us to trade the complexity of the observer implementation for achieved performance. Several examples illustrate that the on-line computational cost of the observer implementation is lower than that of the Kalman filter, whilst the performance is similar.