Abstract
Information filters can process nonlinear systems with uncertain prior knowledge, and the particular square-root form of adaptive filters can improve numerical stability. Based on a square-root decomposition of information matrix and an extra positive definite matrix, the unscented transform and the cubature rule are applied to the information filtering architecture for nonlinear estimation. A class of stable square-root nonlinear information filters is then proposed in this technical note. In addition, the boundedness of their estimation errors is also proven. Results from simulations of filtering a chaotic map demonstrate that the proposed square-root nonlinear filters can improve numerical stability, and has better filtering performance than other information filters.
Original language | English |
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Article number | 6681929 |
Pages (from-to) | 1893-1898 |
Number of pages | 6 |
Journal | IEEE Transactions on Automatic Control |
Volume | 59 |
Issue number | 7 |
DOIs | |
Publication status | Published - 1 Jan 2014 |
Keywords
- Nonlinear estimation
- nonlinear information filter
- numerical stability
- square-root decomposition
ASJC Scopus subject areas
- Control and Systems Engineering
- Computer Science Applications
- Electrical and Electronic Engineering