  We consider multi-sensor fusion estimation for clustered sensor networks.
Both sequential measurement fusion and state fusion estimation methods are
presented. It is shown that the proposed sequential fusion estimation methods
achieve the same performance as the batch fusion one, but are more convenient
to deal with asynchronous or delayed data since they are able to handle the
data that is available sequentially. Moreover, the sequential measurement
fusion method has lower computational complexity than the conventional
sequential Kalman estimation and the measurement augmentation methods, while
the sequential state fusion method is shown to have lower computational
complexity than the batch state fusion one. Simulations of a target tracking
system are presented to demonstrate the effectiveness of the proposed results.
