title: Online parameter estimation for surgical needle steering model.
abstract: 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.
authors.affiliation: Schools of MAE, Nanyang Technological University, Singapore.
authors.forename: Kai Guo
authors.initials: KG
authors.lastname: Yan
authors.forename: Tarun
authors.initials: T
authors.lastname: Podder
authors.forename: Di
authors.initials: D
authors.lastname: Xiao
authors.forename: Yan
authors.initials: Y
authors.lastname: Yu
authors.forename: Tien-I
authors.initials: TI
authors.lastname: Liu
authors.forename: Keck Voon
authors.initials: KV
authors.lastname: Ling
authors.forename: Wan Sing
authors.initials: WS
authors.lastname: Ng
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003238:Connective Tissue / Q000502:physiology*; D004548:Elasticity; D006801:Humans; D008954:Models, Biological; D009339:Needles; D009862:Online Systems; D019919:Prosthesis Implantation / Q000379:methods*; D013314:Stress, Mechanical; D014783:Viscosity