Abstract
This paper investigates the distributed state estimation for a class of linear time-varying systems with intermittent observations in sensor networks. Unlike the existing studies in distributed state estimation, this work considers the scenario where the cross-covariances between different sensors are unavailable and the measurements for state estimation encounter intermittent observations and/or random losses. For this practical scenario, a new sequential covariance intersection-based Kalman consensus filer (SCIKCF) is then developed. We show that, with the proposed SCIKCF, each sensor can achieve consensus estimates regardless of the order of fusion. Furthermore, the stability of the SCIKCF as well as the boundedness of the estimation error and the corresponding error covariances are analysed. Finally, three examples are performed to verify the effectiveness of the proposed SCIKCF.
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 callDisclaimer: 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.
