skip to main content


Search for: All records

Award ID contains: 2313814

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Summary

    In this paper, we propose a filtering algorithm for simultaneously estimating the mode, input and state of hidden mode switched linear stochastic systems with unknown inputs. Using a multiple‐model approach with a bank of linear input and state filters for each mode, our algorithm relies on the ability to find the most probable model as a mode estimate, which we show is possible with input and state filters by identifying a key property, that a particular residual signal we callgeneralized innovationis a Gaussian white noise. We also provide an asymptotic analysis for the proposed algorithm and provide sufficient conditions forasymptoticallyachieving convergence to the true model (consistency), or to the “closest” model according to an information‐theoretic measure (convergence). A simulation example of intention‐aware vehicles at an intersection is given to demonstrate the effectiveness of our approach.

     
    more » « less
  2. Free, publicly-accessible full text available December 13, 2024
  3. Free, publicly-accessible full text available December 13, 2024
  4. Free, publicly-accessible full text available December 13, 2024
  5. Free, publicly-accessible full text available December 1, 2024
  6. Free, publicly-accessible full text available November 22, 2024
  7. Free, publicly-accessible full text available October 4, 2024