Unscented Transformation based estimation of parameters of nonlinear models using heteroscedastic data
作者：
Highlights：
• Derivative free approach of solving gradient weighted least squares problems.
• Improved approximation of the geometric distance for curve fitting.
• Improved performance of proposed technique demonstrated on three benchmarks.
• Monte Carlo used to benchmark the reduction in bias in estimated parameters.
• Relevant to model fitting problems with heteroscedastic data.
摘要
Highlights•Derivative free approach of solving gradient weighted least squares problems.•Improved approximation of the geometric distance for curve fitting.•Improved performance of proposed technique demonstrated on three benchmarks.•Monte Carlo used to benchmark the reduction in bias in estimated parameters.•Relevant to model fitting problems with heteroscedastic data.
论文关键词：Parameter estimation,Model identification,Unscented Transformation,Ellipse fitting,Superellipse fitting,Heteroscedastic data
论文评审过程：Received 12 April 2015, Revised 10 February 2016, Accepted 12 February 2016, Available online 23 February 2016, Version of Record 21 March 2016.
论文官网地址：https://doi.org/10.1016/j.patcog.2016.02.009
