Distributed Filtering for Switched Nonlinear Positive Systems With Missing Measurements Over Sensor Networks

In this paper, the distributed filtering problem is investigated for a class of switched nonlinear positive systems over sensor networks. The randomly varying nonlinearities and missing measurements, which are governed by two mutually independent Bernoulli distributed white sequences, are taken into account. Based on the output measurements of the individual sensor and its neighbors, the distributed filter with positivity constraint is designed to ensure the prescribed average l
_∞
performance index of the estimation error dynamics. Special attention is paid to preserve the positivity of the underlying system as well as the sparseness of the addressed network topology. Sufficient conditions are established on the existence of the desired filters by using the linear programming approach, and the filter gains are subsequently characterized. A simulation example is provided to illustrate the effectiveness of the proposed filtering method.