Yan Kai Guo, Podder Tarun, Xiao Di, Yu Yan, Liu Tien-I, Ling Keck Voon, Ng Wan Sing
Schools of MAE, Nanyang Technological University, Singapore.
Med Image Comput Comput Assist Interv. 2006;9(Pt 1):321-9. doi: 10.1007/11866565_40.
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.
在存在噪声的输入/输出数据情况下估计系统参数，是控制与信号处理领域的一个主要方向。近年来已经提出了许多不同的估计方法。在各种方法中，扩展卡尔曼滤波（EKF）对于估计非线性时变系统的参数非常有用。此外，它可以消除噪声的影响，从而显著提高结果。我们这里的任务是估计弹簧-梁-阻尼针转向模型中的系数。许多研究人员在研究组织变形时采用了这种弹簧-阻尼模型。使用这种模型的一个难点在于估计弹簧和阻尼系数。在此，我们提出了一种使用EKF的在线参数估计器来解决这个问题。本文给出了详细设计。计算机模拟和物理实验表明，该模拟器能够以快速收敛速度准确估计参数，并提高模型效能。
