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On Analysis of Active Querying for Recursive State Estimation.

Aziz Koçanaoğulları1, Murat Akçakay2, Deniz Erdoğmuş1

  • 1Northeastern University.

IEEE Signal Processing Letters
|December 25, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces an active querying method using mutual information maximization for improved state estimation in dynamic systems. The proposed algorithm efficiently solves the exact optimization problem, enhancing accuracy and reducing estimation time.

Keywords:
active queryingmutual informationquery selectionrecursive state estimation

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Area of Science:

  • Control Systems Engineering
  • Information Theory
  • Signal Processing

Background:

  • State estimation in dynamic systems relies on recursive measurements.
  • Query selection is crucial for enhancing estimation accuracy and efficiency.
  • Current methods optimize approximations, not exact objectives.

Purpose of the Study:

  • To propose an active querying procedure using mutual information maximization for recursive state estimation.
  • To demonstrate that mutual information generalizes variance-based query selection.
  • To solve the exact optimization problem for query selection.

Main Methods:

  • Formulating mutual information maximization as a combinatorial optimization problem.
  • Showing the sub-modular nature of the objective function for efficient greedy solutions.
  • Developing a query selection algorithm with guaranteed convergence bounds.

Main Results:

  • Mutual information generalizes variance-based query selection, especially for unimodal distributions.
  • The proposed algorithm efficiently solves the exact query selection optimization problem.
  • The algorithm demonstrated effective performance in a brain-computer interface typing system.

Conclusions:

  • The developed active querying strategy significantly improves state estimation in dynamic systems.
  • The combinatorial optimization approach provides an efficient and guaranteed method for query selection.
  • This research offers advancements in active dynamic systems and brain-computer interfaces.