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Batch Bayesian auto-tuning for nonlinear Kalman estimators.

Cristovao Freitas Iglesias1, Miodrag Bolic2

  • 1School of Electrical Engineering and Computer Science (EECS), University of Ottawa, Ottawa, ON, K1N 6N5, Canada. cfrei096@uottawa.ca.

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Summary
This summary is machine-generated.

Batch Bayesian Auto-Tuning (BAT) optimizes nonlinear Kalman estimators (NKEs) by using all data for tuning. This novel approach improves NKE performance and reliability in real-world applications.

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Area of Science:

  • Control Systems Engineering
  • Statistical Signal Processing
  • Computational Biology

Background:

  • Nonlinear Kalman Estimators (NKEs) require precise tuning of five key parameters for optimal performance.
  • Traditional auto-tuning methods for NKEs are limited by reliance on ground truth models or partial data, leading to inefficiencies and errors.
  • Manual tuning is time-consuming and susceptible to human error, hindering practical application.

Purpose of the Study:

  • To introduce a novel auto-tuning approach, Batch Bayesian Auto-Tuning (BAT), for optimizing all components of Nonlinear Kalman Estimators (NKEs).
  • To enable the utilization of all available measured data in the NKE tuning process, overcoming limitations of existing methods.
  • To enhance the accuracy, consistency, and reliability of NKE estimations in practical scenarios.

Main Methods:

  • Developed Batch Bayesian Auto-Tuning (BAT), a method that defines a comprehensive posterior distribution for all NKE components using all available measured data.
  • Leveraged the equivalence between posterior distributions in batch and recursive Bayesian inference to perform tuning outside the NKE's recursive process.
  • Validated the BAT approach using a synthetic bioprocess dataset.

Main Results:

  • Empirical validation demonstrated that BAT significantly improves the consistency and accuracy of NKE estimations compared to baseline methods.
  • The BAT approach effectively utilizes all available measured data for tuning, unlike traditional methods.
  • BAT successfully optimized all five key NKE components, including process noise covariance, measurement noise covariance, initial state noise covariance, initial state conditions, and dynamic model parameters.

Conclusions:

  • Batch Bayesian Auto-Tuning (BAT) offers a robust and efficient solution for optimizing Nonlinear Kalman Estimators (NKEs).
  • The method enhances NKE performance by enabling comprehensive data utilization during the tuning phase.
  • BAT presents a significant advancement for improving the reliability and practical applicability of NKEs in various fields.