• Corpus ID: 27050809

EE 226 a-Summary of Lecture 13 and 14 Kalman Filter : Convergence

@inproceedings{Walrand2005EE2A,
  title={EE 226 a-Summary of Lecture 13 and 14 Kalman Filter : Convergence},
  author={Jean C. Walrand},
  year={2005}
}
I. SUMMARY Here are the key ideas and results of this important topic. • SectionII reviews Kalman Filter. • A system is observable if its state can be determined from its outputs (after some delay). • A system is reachable if there are inputs to drive it to any state. • We explore the evolution of the covariance in a linear system in Section IV. • The error covariance of a Kalman Filter is bounded if the system is observable. • The covariance increases if it starts from zero. • If a system is… 
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5 Citations
Background Citations
3

5 Citations

Non-gaussian estimation and observer-based feedback using the Gaussian Mixture Kalman and Extended Kalman Filters

This manuscript uses a Gaussian Mixture Model (GMM) to characterize the prior non-Gaussian distribution, and applies the Kalman filter update to estimate the state with uncertainty and uses the resulting estimate for feedback control.

Extensions to the Theory of Widely Linear Complex Kalman Filtering

A WLCKF which can deal with more general dynamical models of complex-valued states and measurements than the W LCKFs in Mandic and Goh is proposed and has an equivalency with the corresponding dual channel real KF.

Fast sequential parameter inference for dynamic state space models

A new methodology of sequential updating makes the calculation of posterior both fast and accurate, while it can be applied to a wide class of models existing in literature, and can be used for any existing state estimation algorithm.

Quantized innovations Kalman filter: stability and modificationwith scaling quantization

The stability of quantized innovations Kalman filtering (QIKF) is analyzed. In the analysis, the correlation between quantization errors and measurement noises is considered. By taking the…

Convergence of Kalman filter with quantized innovations

This work provides a convergence analysis for the estimate error covariance of Kaiman filtering based on quantized measurement innovations (QIKF) and the quantitative Kaiman filter for the original system is equivalent to a Kalman-like filtering for the equivalent state-observation system.

References

Gallager:Stochastic Processes: A Conceptual Approach

  • 2001