State estimation for a class of nonlinear discrete-time complex networks subject to false data injection attacks

This paper is concerned with the state estimation problem for a class of nonlinear discrete-time complex networks subject to false data injection attacks. By utilizing Bernoulli random binary distributed white sequences, the false data injection attack model is established to describe the characteristics of false data injection attacks applying to the complex networks under consideration. An estimator is designed to guarantee the ultimate boundedness of the estimation error in mean square. By employing stochastic analysis approach, sufficient conditions are derived for the existence of the desired estimators whose gains are parameterized by minimizing an upper bound of the output variance of the estimation errors. Finally, a numerical example is given to illustrate the effectiveness of the results.