• Corpus ID: 27050809

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

  title={EE 226 a-Summary of Lecture 13 and 14 Kalman Filter : Convergence},
  author={Jean C. Walrand},
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… 

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.

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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.

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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.

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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

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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.


Gallager:Stochastic Processes: A Conceptual Approach

  • 2001