Abstract
This paper addresses the distributed state estimation over a large scale sensor network wherein each sensor may have an unknown measurement bias. In order to mitigate the influence of biases to state estimation, a bias estimator only depending on the structure of local sensor is constructed to ensure the algorithm's scalability for sensor networks. It is proved that the proposed bias estimator can effectively realize the bias estimation for the slow time-varying measurement bias. In addition, a distributed Kalman filter embedded with local bias estimator is developed to efficiently correct the biased measurements. Both consistency and stability of the filter are guaranteed under a general collective observability condition for linear time-varying systems. A numerical simulation is provided to illustrate the effectiveness of the developed results.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Transactions on Signal and Information Processing over Networks
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
