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