Abstract
This paper presents a performance comparison of Kaiman-filter-based distributed state estimations techniques for large-scale systems. Three distributed Kaiman filter schemes are selected to be implemented in simulation, namely, distributed and decentralized Kaiman filter (DDKF), distributed Kaiman filter with state consensus (DKF-SC) and distributed Kaiman filter with diffusion strategy (DKF-DS), using the heat rod dynamical system as benchmark. The heat conduction and convection in a solid rod dynamic is used as test bed. The comparison is performed by evaluating prediction error and convergence of each scheme, considering the partition of the system into two, three and four subsystems.
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.
