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A jump filter for uncertain dynamic systems with dropouts

In this work, we consider a state estimation problem for linear uncertain discrete-time systems over a network with dropouts. The uncertainty of the plant model is described by a norm-bounded time-varying parameter that affects the system matrix. We design a jump filter that minimizes an upper bound of the trace of the state estimation error covariance for all possible parameter uncertainties and whose gains are selected from a precalculated finite set depending on the possible measurement reception scenarios. The presented procedure allows us to relate the complexity of the jump filter to the achievable estimation performance.