Abstract
A new estimation algorithm is derived and appraised for nonlinear systems. The notion and measures of information are defined and this leads to a discussion of the algebraic equivalent of the Kalman filter, the linear information filter. Examples of dynamic systems are simulated to illustrate the algebraic equivalence of the Kalman and information filters. The benefits of information space are also explored. Estimation for systems with nonlinearities is then considered starting with the extended Kalman filter. Linear information space is extended to nonlinear information space by deriving the extended information filter. The advantages of the extended information filter over the extended Kalman filter are demonstrated for systems involving both nonlinear state evolution and nonlinear observations.
| Original language | English |
|---|---|
| Pages (from-to) | 1329-1333 |
| Number of pages | 5 |
| Journal | Proceedings of the American Control Conference |
| Volume | 2 |
| Publication status | Published - 1999 |
| Externally published | Yes |
| Event | Proceedings of the 1999 American Control Conference (99ACC) - San Diego, CA, USA Duration: 2 Jun 1999 → 4 Jun 1999 |
ASJC Scopus subject areas
- Electrical and Electronic Engineering