EECI08: State Estimation and Sensor Fusion

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This lecture provides a review of key results in state estimation and sensor fusion that will be built upon in future lectures. We briefly summarize Kalman filtering and and describe how to use Kalman filters to obtain optimal sensor fusion in a centralized setting. We also discuss several variants that are of use in a computationally-rich, networked environment: information filters, moving horizon estimation and particle filters. Extensions to networked sensors and distributed sensing are discussed in the follow two lectures.

Lecture Materials


  • Chapters 4-7 - This is a set of notes from a course on optimization-based control that covers much of the background material required for this lecture, including random processes and Kalman filters.

  • Optimal Filtering, B.D.O Anderson and J.B. Moore. Dover Books on Engineering, 2005. A reissue of a book from 1979. It contains a detailed mathematical presentation of filtering problems and the Kalman filter. A very good book.

Additional Information

  • The Kalman Filter, G. Welch and G. Bishop. A webpage with many links on Kalman filter.