Optimized State Estimation for Complex Networks with Sensor Fault and Probability-Based Quantization

This paper investigates the optimized state estimation (OSE) problem for uncertain coupled delay complex networks (UCDCNs) subject to sensor fault and probability-based quantization. A random variable with known statistical characteristics and a uniformed distributed random parameter are adopted respectively to describe the model uncertainty and sensor fault. Taking the bandwidth-limited channel into account, a probability-based quantization strategy is employed to process transmitted data. A coupled-delay-dependent predictor-estimator is constructed to guarantee that the covariance upper bounds of prediction error (PE) and estimation error (EE) can be obtained by resolving recursive matrix equations and the predictor-estimator gain (PEG) can be determined via optimizing the trace of the covariance upper bound of EE (CUBEE). Finally, an efficient example is presented to demonstrate the validity of the developed OSE algorithm.