Distributed information fusion estimation for sensor networks with nonuniform sampling rates

This paper investigates the multi-sensor information fusion estimation problem for sensor networks with nonuniform sampling rates. The measurements are sampled asynchronously by the various sensors with nonuniform sampling rates. Then, each sensor in the network acts also as an estimator and collects measurements from its neighbors to generate estimates by applying a distributed measurement fusion approach and the Kalman filtering technique. It is shown that the proposed fusion estimator is equivalent to that designed by using the measurement augmentation approach. A numerical example is provided to demonstrate the effectiveness of the proposed design method.