Abstract
Abstract : The purpose of this report is to introduce an adaptive estimation and parameter identification scheme which the authors call Multiple Model Estimation Algorithm (MMEA). The MMEA consists of a bank of Kalman filters with each matched to a possible parameter vector. The state estimates generated by these Kalman filters are then combined using a weighted sum with the a posteriori hypothesis probabilities as weighting factors. If one of the selected parameter vectors coincides with the true parameter vector, this algorithm gives the minimum variance state and parameter estimates. Algorithms for filtering, smoothing, and prediction are derived for linear and nonlinear systems. They are described in a tutorial fashion with results stated explicitly so that they can be readily used for computer implementation. Approaches for the extension of MMEA to a more general class of adaptive estimation problems are outlined. Several further research topics are also suggested.
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.
