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

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    Author
    Shin, J; Zhong, Y; Oetomo, D; Gu, C
    Date
    2018-05-01
    Source Title
    Sensors
    Publisher
    MDPI
    University of Melbourne Author/s
    Oetomo, Denny
    Affiliation
    Mechanical Engineering
    Metadata
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    Document Type
    Journal Article
    Citations
    Shin, J., Zhong, Y., Oetomo, D. & Gu, C. (2018). Random Weighting, Strong Tracking, and Unscented Kalman Filter for Soft Tissue Characterization. SENSORS, 18 (5), https://doi.org/10.3390/s18051650.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/255200
    DOI
    10.3390/s18051650
    Abstract
    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.

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