Next Article in Journal
Registration of Panoramic/Fish-Eye Image Sequence and LiDAR Points Using Skyline Features
Previous Article in Journal
An Observation Capability Semantic-Associated Approach to the Selection of Remote Sensing Satellite Sensors: A Case Study of Flood Observations in the Jinsha River Basin
Open AccessArticle

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

1
School of Engineering, RMIT University, Bundoora, VIC 3083, Australia
2
Department of Mechanical Engineering, University of Melbourne, Parkville, VIC 3010, Australia
3
City of Whittlesea, Mill Park, VIC 3082, Australia
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(5), 1650; https://doi.org/10.3390/s18051650
Received: 1 April 2018 / Revised: 17 May 2018 / Accepted: 17 May 2018 / Published: 21 May 2018
(This article belongs to the Section Physical Sensors)
This paper presents a new nonlinear filtering method based on the Hunt-Crossley model for online nonlinear soft tissue characterization. This method overcomes the problem of performance degradation in the unscented Kalman filter due to contact model error. It adopts the concept of Mahalanobis distance to identify contact model error, and further incorporates a scaling factor in predicted state covariance to compensate identified model error. This scaling factor is determined according to the principle of innovation orthogonality to avoid the cumbersome computation of Jacobian matrix, where the random weighting concept is adopted to improve the estimation accuracy of innovation covariance. A master-slave robotic indentation system is developed to validate the performance of the proposed method. Simulation and experimental results as well as comparison analyses demonstrate that the efficacy of the proposed method for online characterization of soft tissue parameters in the presence of contact model error. View Full-Text
Keywords: soft tissue characterization; Hunt-Crossley model; unscented Kalman filter; contact model error; strong tracking; and random weighting soft tissue characterization; Hunt-Crossley model; unscented Kalman filter; contact model error; strong tracking; and random weighting
Show Figures

Figure 1

MDPI and ACS Style

Shin, J.; Zhong, Y.; Oetomo, D.; Gu, C. Random Weighting, Strong Tracking, and Unscented Kalman Filter for Soft Tissue Characterization. Sensors 2018, 18, 1650.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop