Fusion Estimation for Sensor Networks With Nonuniform Estimation Rates

This paper investigates the multi-sensor fusion estimation problem for wireless sensor networks with nonuniform estimation rates. First, each sensor generates local estimates with two rates, namely, a fast rate and a slow rate according to its power situation, where the estimation rates among the sensors are allowed to be different from each other. Second, a fusion rule with matrix weights is designed for each sensor to fuse available local estimates generated at different time scales, and a set of recursive equations are presented to compute estimation error cross-covariances. The fusion algorithm is applicable to both cases where the measurement noises are mutually correlated and are uncorrelated, and is also applicable to the case where the sensors are not time-synchronized. Two types of estimators are designed according to different considerations of design complexity and computation costs, and convergence analysis for the type II estimators is also presented. Finally, two illustrative examples are given to demonstrate the effectiveness of the proposed estimators.