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

Shin, Jaehyun; Zhong, Yongmin; Oetomo, Denny; Gu, Chengfan

doi:10.3390/s18051650

Cited by 5 publications

(3 citation statements)

References 28 publications

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“…The Kalman Filter is a Linear Quadratic Estimation (LQE) method that is widely used in various fields, including navigation [ 10 ], signal processing [ 11 ], control systems [ 12 ], biological tissue identification [ 13 ], etc. The Kalman Filter is based on the state–space model, and for linear systems and Gaussian noise, this model is usually represented by the following two equations [ 8 ]:…”

Section: Methodsmentioning

confidence: 99%

Advancements in Buoy Wave Data Processing through the Application of the Sage–Husa Adaptive Kalman Filtering Algorithm

Jiang,

Chen,

Liu

2023

Sensors

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In this paper, we propose a combined filtering method rooted in the application of the Sage–Husa Adaptive Kalman filtering, designed specifically to process wave sensor data. This methodology aims to boost the measurement precision and real-time performance of wave parameters. (1) This study delineates the basic principles of the Kalman filter. (2) We discuss in detail the methodology for analyzing wave parameters from the collected wave acceleration data, and deeply study the key issues that may arise during this process. (3) To evaluate the efficacy of the Kalman filter, we have designed a simulation comparison encompassing various filtering algorithms. The results show that the Sage–Husa Adaptive Kalman Composite filter demonstrates superior performance in processing wave sensor data. (4) Additionally, in Chapter 5, we designed a turntable experiment capable of simulating the sinusoidal motion of waves and carried out a detailed errors analysis associated with the Kalman filter, to facilitate a deep understanding of potential problems that may be encountered in practical application, and their solutions. (5) Finally, the results reveal that the Sage–Husa Adaptive Kalman Composite filter improved the accuracy of effective wave height by 48.72% and the precision of effective wave period by 23.33% compared to traditional bandpass filter results.

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Section: Methodsmentioning

confidence: 99%

Advancements in Buoy Wave Data Processing through the Application of the Sage–Husa Adaptive Kalman Filtering Algorithm

Jiang,

Chen,

Liu

2023

Sensors

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“…Based on the above, the adaptive scaling factor Φ can be attained by matching the predicted state covariance given by (17) with its theoretical value given by (22):…”

Section: Recursive Adaptive Unscented Kalman Filtermentioning

confidence: 99%

“…The authors also studied a UKF based on the H-C model for soft tissue characterization [16], but without considering the UKF requirement of accurate system models. Just recently, the authors reported an improved UKF to handle the error of the H-C contact model using the scaling factor [17]. However, this method is based on the assumption that the measurement model is accurate, which does not correspond to the realworld situation with errors involved in both contact and measurement models.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Nonlinear Characterization of Soft Tissue Mechanical Properties

Shin

Zhong

2020

Journal of Sensors

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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.

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Estimation of flexible space tether state based on end measurement by finite element Kalman filter state estimator

Zhu

2021

Advances in Space Research

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

Cited by 5 publications

References 28 publications

Advancements in Buoy Wave Data Processing through the Application of the Sage–Husa Adaptive Kalman Filtering Algorithm

Advancements in Buoy Wave Data Processing through the Application of the Sage–Husa Adaptive Kalman Filtering Algorithm

Real-Time Nonlinear Characterization of Soft Tissue Mechanical Properties

Estimation of flexible space tether state based on end measurement by finite element Kalman filter state estimator

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