Distributed covariance intersection fusion in clustered sensor networks with different sampling rates

Abstract

This paper is concerned with the fusion estimation problem in clustered sensor networks (CSN). A set of sensors are divided into several clusters and independently observe outputs of a plant with different sampling rates. During each estimating interval, each local estimator collects sampled information from sensors in its area and generates a local estimate at the estimating instant. A fusion center (FC) is connected with all the estimators to fuse the local estimates by using the covariance intersection (CI) method. An illustrative example is provided to demonstrate the effectiveness of the proposed results.