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<p>Accurate estimation of Radio frequency (RF) cavity detuning is one of the important aims for controlling and monitoring of the particle accelerator used for beam acceleration.&#xa0;A methodological research is needed to achieve the aim of obtaining an accurate, unbiased and consistent detuning estimator.&#xa0;In the past, various estimation techniques for determination of RF cavity detuning have been applied.&#xa0;Methods like least squares, cavity parameter identification, linear and nonlinear observers have been employed for normal conducting and superconducting RF cavities.&#xa0;Apart from other methods, the Kalman filter has been employed by a few practitioners in the case of superconducting cavities.&#xa0;The Kalman filter is based on a stochastic state space framework, and it accounts for practical issues like model uncertainty and measurement noise in estimation of states and parameters.&#xa0;The behavioral model of a superconducting RF cavity is nonlinear due to the squared dependence of cavity detuning on the electric field inside it.&#xa0;Detuning being unmeasurable, is estimated from models and measured quantities.&#xa0;In general, a nonlinear system needs to use a nonlinear estimator for accuracy and low variability estimates.&#xa0;In this paper, we aim to quantify the uncertainty in detuning estimated by Kalman filter, extended Kalman filter and unscented Kalman filter for superconducting RF cavity.&#xa0;This aim has been achieved by characterization of noise from simulated results.&#xa0;Moments of the underlying probability distribution function of noise are determined as noise propagates through these filters.&#xa0;Tools such as quantile plots and descriptive statistics are used in this work to compare various cases quantitatively and arrive at important conclusions.</p>

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Detuning Estimation Measurement Uncertainty Quantification Using Descriptive Statistics

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月11日

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  • Rajesh T. Keshwani,
  • S. Mukhopadhyay,
  • R. D. Gudi,
  • Gopal Joshi

摘要

Accurate estimation of Radio frequency (RF) cavity detuning is one of the important aims for controlling and monitoring of the particle accelerator used for beam acceleration. A methodological research is needed to achieve the aim of obtaining an accurate, unbiased and consistent detuning estimator. In the past, various estimation techniques for determination of RF cavity detuning have been applied. Methods like least squares, cavity parameter identification, linear and nonlinear observers have been employed for normal conducting and superconducting RF cavities. Apart from other methods, the Kalman filter has been employed by a few practitioners in the case of superconducting cavities. The Kalman filter is based on a stochastic state space framework, and it accounts for practical issues like model uncertainty and measurement noise in estimation of states and parameters. The behavioral model of a superconducting RF cavity is nonlinear due to the squared dependence of cavity detuning on the electric field inside it. Detuning being unmeasurable, is estimated from models and measured quantities. In general, a nonlinear system needs to use a nonlinear estimator for accuracy and low variability estimates. In this paper, we aim to quantify the uncertainty in detuning estimated by Kalman filter, extended Kalman filter and unscented Kalman filter for superconducting RF cavity. This aim has been achieved by characterization of noise from simulated results. Moments of the underlying probability distribution function of noise are determined as noise propagates through these filters. Tools such as quantile plots and descriptive statistics are used in this work to compare various cases quantitatively and arrive at important conclusions.