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This paper addresses the design of a state observer for networked systemswith random delays and dropouts. The model of plant and network covers thecases of multiple sensors, out-of-sequence and buffered measurements. Themeasurement outcomes over a finite interval model the network measurementreception scenarios, which follow a Markov distribution. We present a tractableoptimization problem to precalculate off-line a finite set of gains of jumpobservers. The proposed procedure allows us to trade the complexity of theobserver implementation for achieved performance. Several examples illustratethat the online computational cost of the observer implementation is lower thanthat of the Kalman filter, whilst the performance is similar.

Performance vs complexity trade-offs for Markovian networked jump estimators