Adaptive unscented kalman filter for online soft tissues characterization

Jaehyun Shin, Yongmin Zhong, Julian Smith, Chengfan Gu

Research output: Contribution to journalConference articleResearchpeer-review

Abstract

Online soft tissue characterization is important for robotic-assisted minimally invasive surgery to achieve precise and stable robotic control with haptic feedback. This paper presents a new adaptive unscented Kalman filter based on the nonlinear Hunt-Crossley model for online soft tissue characterization without requiring the characteristics of system noise. This filter incorporates the concept of Sage windowing in the traditional unscented Kalman filter to adaptively estimate system noise covariance using predicted residuals within a time window. In order to account for the inherent relationship between the current and previous states of soft tissue deformation involved in robotic-assisted surgery and improve the estimation performance, a recursive estimation of system noise covariance is further constructed by introducing a fading scaling factor to control the contributions between noise covariance estimations at current and previous time points. The proposed adaptive unscented Kalman filter overcomes the limitation of the traditional unscented Kalman filter in requiring the characteristics of system noise. Simulations and comparisons show the efficacy of the suggested nonlinear adaptive unscented Kalman filter for online soft tissue characterization.

Original languageEnglish
Article number1740014
Number of pages10
JournalJournal of Mechanics in Medicine and Biology
Volume17
Issue number7
DOIs
Publication statusPublished - 1 Nov 2017
EventInternational Workshop on Biological Mechanics - Guangzhou, China
Duration: 17 Oct 201720 Oct 2017

Keywords

  • Hunt-Crossley model
  • parameter estimation
  • Robotic-assisted surgery
  • soft tissue characterization
  • system noise statistics
  • unscented Kalman filter

Cite this

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title = "Adaptive unscented kalman filter for online soft tissues characterization",
abstract = "Online soft tissue characterization is important for robotic-assisted minimally invasive surgery to achieve precise and stable robotic control with haptic feedback. This paper presents a new adaptive unscented Kalman filter based on the nonlinear Hunt-Crossley model for online soft tissue characterization without requiring the characteristics of system noise. This filter incorporates the concept of Sage windowing in the traditional unscented Kalman filter to adaptively estimate system noise covariance using predicted residuals within a time window. In order to account for the inherent relationship between the current and previous states of soft tissue deformation involved in robotic-assisted surgery and improve the estimation performance, a recursive estimation of system noise covariance is further constructed by introducing a fading scaling factor to control the contributions between noise covariance estimations at current and previous time points. The proposed adaptive unscented Kalman filter overcomes the limitation of the traditional unscented Kalman filter in requiring the characteristics of system noise. Simulations and comparisons show the efficacy of the suggested nonlinear adaptive unscented Kalman filter for online soft tissue characterization.",
keywords = "Hunt-Crossley model, parameter estimation, Robotic-assisted surgery, soft tissue characterization, system noise statistics, unscented Kalman filter",
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language = "English",
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Adaptive unscented kalman filter for online soft tissues characterization. / Shin, Jaehyun; Zhong, Yongmin; Smith, Julian; Gu, Chengfan.

In: Journal of Mechanics in Medicine and Biology, Vol. 17, No. 7, 1740014, 01.11.2017.

Research output: Contribution to journalConference articleResearchpeer-review

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AU - Shin, Jaehyun

AU - Zhong, Yongmin

AU - Smith, Julian

AU - Gu, Chengfan

PY - 2017/11/1

Y1 - 2017/11/1

N2 - Online soft tissue characterization is important for robotic-assisted minimally invasive surgery to achieve precise and stable robotic control with haptic feedback. This paper presents a new adaptive unscented Kalman filter based on the nonlinear Hunt-Crossley model for online soft tissue characterization without requiring the characteristics of system noise. This filter incorporates the concept of Sage windowing in the traditional unscented Kalman filter to adaptively estimate system noise covariance using predicted residuals within a time window. In order to account for the inherent relationship between the current and previous states of soft tissue deformation involved in robotic-assisted surgery and improve the estimation performance, a recursive estimation of system noise covariance is further constructed by introducing a fading scaling factor to control the contributions between noise covariance estimations at current and previous time points. The proposed adaptive unscented Kalman filter overcomes the limitation of the traditional unscented Kalman filter in requiring the characteristics of system noise. Simulations and comparisons show the efficacy of the suggested nonlinear adaptive unscented Kalman filter for online soft tissue characterization.

AB - Online soft tissue characterization is important for robotic-assisted minimally invasive surgery to achieve precise and stable robotic control with haptic feedback. This paper presents a new adaptive unscented Kalman filter based on the nonlinear Hunt-Crossley model for online soft tissue characterization without requiring the characteristics of system noise. This filter incorporates the concept of Sage windowing in the traditional unscented Kalman filter to adaptively estimate system noise covariance using predicted residuals within a time window. In order to account for the inherent relationship between the current and previous states of soft tissue deformation involved in robotic-assisted surgery and improve the estimation performance, a recursive estimation of system noise covariance is further constructed by introducing a fading scaling factor to control the contributions between noise covariance estimations at current and previous time points. The proposed adaptive unscented Kalman filter overcomes the limitation of the traditional unscented Kalman filter in requiring the characteristics of system noise. Simulations and comparisons show the efficacy of the suggested nonlinear adaptive unscented Kalman filter for online soft tissue characterization.

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KW - parameter estimation

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KW - soft tissue characterization

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KW - unscented Kalman filter

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