scholarly journals Random Weighting, Strong Tracking, and Unscented Kalman Filter for Soft Tissue Characterization

Sensors ◽  
2018 ◽  
Vol 18 (5) ◽  
pp. 1650 ◽  
Author(s):  
Jaehyun Shin ◽  
Yongmin Zhong ◽  
Denny Oetomo ◽  
Chengfan Gu
Keyword(s):  
Kalman Filter ◽  
Soft Tissue ◽  
Tissue Characterization ◽  
Unscented Kalman Filter ◽  
Random Weighting

ADAPTIVE UNSCENTED KALMAN FILTER FOR ONLINE SOFT TISSUES CHARACTERIZATION

2017 ◽  
Vol 17 (07) ◽  
pp. 1740014
Author(s):  
JAEHYUN SHIN ◽  
YONGMIN ZHONG ◽  
JULIAN SMITH ◽  
CHENGFAN GU
Keyword(s):  
Kalman Filter ◽  
Soft Tissue ◽  
Tissue Characterization ◽  
Haptic Feedback ◽  
Unscented Kalman Filter ◽  
Recursive Estimation ◽  
Robotic Assisted ◽  
System Noise ◽  
Adaptive Unscented Kalman Filter ◽  
Noise Covariance

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.

A NEW PARAMETER ESTIMATION METHOD FOR ONLINE SOFT TISSUE CHARACTERIZATION

2016 ◽  
Vol 16 (08) ◽  
pp. 1640019 ◽  
Author(s):  
JAEHYUN SHIN ◽  
YONGMIN ZHONG ◽  
JULIAN SMITH ◽  
CHENGFAN GU
Keyword(s):  
Soft Tissue ◽  
Linear Regression ◽  
Tissue Characterization ◽  
Soft Tissues ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Regression Method ◽  
Linear Regression Method ◽  
Non Linear

Dynamic soft tissue characterization is of importance to robotic-assisted minimally invasive surgery. The traditional linear regression method is unsuited to handle the non-linear Hunt–Crossley (HC) model and its linearization process involves a linearization error. This paper presents a new non-linear estimation method for dynamic characterization of mechanical properties of soft tissues. In order to deal with non-linear and dynamic conditions involved in soft tissue characterization, this method improves the non-linearity and dynamics of the HC model by treating parameter [Formula: see text] as independent variable. Based on this, an unscented Kalman filter is developed for online estimation of soft tissue parameters. Simulations and comparison analysis demonstrate that the proposed method is able to estimate mechanical parameters for both homogeneous tissues and heterogeneous and multi-layer tissues, and the achieved performance is much better than that of the linear regression method.

scholarly journals Real-Time Nonlinear Characterization of Soft Tissue Mechanical Properties

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Jaehyun Shin ◽  
Yongmin Zhong ◽  
Chengfan Gu
Keyword(s):  
Soft Tissue ◽  
Adaptive Filtering ◽  
Tissue Characterization ◽  
Haptic Feedback ◽  
Unscented Kalman Filter ◽  
Weighting Factor ◽  
Measurement Noise ◽  
Robotic Assisted ◽  
Robotic Assisted Surgery ◽  
Noise Statistics

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 nonlinear recursive adaptive filtering methodology for online nonlinear soft tissue characterization. An adaptive unscented Kalman filter is developed based on the Hunt-Crossley model by windowing approximation to online estimate system and measurement noise covariances. To improve the accuracy of noise covariance estimations, a recursive formulation is subsequently developed for estimation of system and measurement noise covariances by introducing a weighting factor. This weighting factor is further modified to accommodate noise statistics of large variation which could be caused by rupture events and geometric discontinuities in robotic-assisted surgery. Simulations, experiments, and comparison analyses demonstrate that the proposed nonlinear recursive adaptive filtering methodology can characterize soft tissue parameters in the presence of system or measurement noise statistics in both small and large variations for robotic-assisted surgery. The proposed methodology can effectively estimate soft tissue parameters under system and measurement noises in both small and large variations, leading to improved filtering accuracy and robustness in comparison with UKF.

Adaptive unscented Kalman filter based on maximum posterior and random weighting

2017 ◽  
Vol 71 ◽  
pp. 12-24 ◽  
Author(s):  
Zhaohui Gao ◽  
Dejun Mu ◽  
Shesheng Gao ◽  
Yongmin Zhong ◽  
Chengfan Gu
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Adaptive Unscented Kalman Filter ◽  
Random Weighting
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