Recursive state estimation for time-varying complex networks subject to missing measurements and stochastic inner coupling under random access protocol - Zhang, Hongxu; Hu, Jun; Liu, Hongjian; Yu, Xiaoyang; Liu, Fengqiu (Neurocomputing (Amsterdam), 2019)
This paper discusses the variance-constrained state estimation problem for time-varying nonlinear complex networks subject to missing measurements and stochastic inner coupling. The stochastic inner coupling is depicted by the multiplicative noise. The phenomenon of the missing measurement is characterized by a set of mutually independent Bernoulli distributed random variables, where the occurrence probabilities could be uncertain. Moreover, in order to avoid the data collisions, the random access protocol (RAP) scheduling is employed to determine which node selected by RAP scheduling with certain probability can use the communication network at each time step. By solving two recursive matrix equations, an optimized upper bound of the estimation error covariance is presented by properly taking the RAP scheduling into consideration. Subsequently, the desired form of the estimator gain is provided guaranteeing the minimization of the trace of the obtained upper bound. Moreover, the monotonicity analysis concerning on the deterministic occurrence probability and the algorithm performance is presented. Finally, a numerical example is utilized to illustrate the validity and correctness of the developed optimal state estimation strategy.