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This paper is concerned with the problem of H ∞ filtering for discrete-time Markov jump linear system with parametric uncertainties and quantized measurements, when the jumping mode information is not accessible. By converting the quantized errors into a sector-bounded nonlinearity, the parametric uncertainties and measurements quantization are dealt with in a unified framework. The mode-independent H ∞ filter is designed, and sufficient conditions are established via Lyapunov function approach, such that for all possible uncertain parameters and quantization errors, the resulting filtering error system is robustly stochastically stable and achieves a guaranteed H ∞ filtering error performance index. A numerical example is provided to demonstrate the feasibility and effectiveness of the proposed approach. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This study is concerned with the problem of state estimation for networked systems with a Markov plant considering the measurement uncertainties. The measurements suffer from both the randomly occurring missing phenomenon and the quantisation effects. Taking into account the statistical knowledge of the quantised measurements, an approximate minimum mean square error estimate algorithms is derived based on Gaussian assumption, which is referred to as interacting multiple model Monte Carlo (IMMMC) algorithm. A quantised measurement expectation calculated by Monte Carlo sampling method is embedded into the Kalman filter under the IMM framework. A simulation example is provided demonstrating that IMMMC is computationally appealing and presents better estimate performance than the previous algorithms. Moreover, IMMMC has better mode following ability and can clearly distinguish the occurrence of measurements missing. [ABSTRACT FROM AUTHOR]