Online Parameter Estimation for Surgical Needle Steering Model

  • Kai Guo Yan
  • Tarun Podder
  • Di Xiao
  • Yan Yu
  • Tien-I Liu
  • Keck Voon Ling
  • Wan Sing Ng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4190)


Estimation of the system parameters, given noisy input/output data, is a major field in control and signal processing. Many different estimation methods have been proposed in recent years. Among various methods, Extended Kalman Filtering (EKF) is very useful for estimating the parameters of a nonlinear and time-varying system. Moreover, it can remove the effects of noises to achieve significantly improved results. Our task here is to estimate the coefficients in a spring-beam-damper needle steering model. This kind of spring-damper model has been adopted by many researchers in studying the tissue deformation. One difficulty in using such model is to estimate the spring and damper coefficients. Here, we proposed an online parameter estimator using EKF to solve this problem. The detailed design is presented in this paper. Computer simulations and physical experiments have revealed that the simulator can estimate the parameters accurately with fast convergent speed and improve the model efficacy.


Online parameter estimation spring/damper model EKF 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kai Guo Yan
    • 1
  • Tarun Podder
    • 3
  • Di Xiao
    • 1
  • Yan Yu
    • 3
  • Tien-I Liu
    • 4
  • Keck Voon Ling
    • 2
  • Wan Sing Ng
    • 1
  1. 1.Schools of MAENanyang Technological UniversitySingapore
  2. 2.Schools of EEENanyang Technological UniversitySingapore
  3. 3.Department of Radiation OncologyUniversity of RochesterU.S.A.
  4. 4.Computer Integrated Manufacturing LabCalifornia State UniversitySacramentoU.S.A.

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