基于各点异性理论的椭圆拟合算法

分析椭圆拟合应用中常用算法对噪声过于敏感、抗干扰能力差的缺点，提出一种鲁棒性较强的椭圆拟合算法。采用各点异性回归技术，建立误差与变量有关的(EIV)模型，根据数据矢量观测集合最优地估计线性EIV模型参数和数据矢量真值集合。实验结果表明，该算法精确度高，当初始值与真实值差距较大时，仍然可以快速、稳定地收敛。

Ellipse Fitting Algorithm Based on Heteroscedastic Theory

This paper analyzes the usual algorithms with less anti-jamming ability which are sensitive to the effect of noise in the application of ellipse fitting, and proposes a more robust ellipse fitting algorithm. It utilizes the heteroscedastic regression technique to create the Errors-In-Variables (EIV) model. According to the observation of data vector, the optimal algorithm is found to obtain the optimal estimations of EIV model parameters and the truth-value of the observed data vector. Experimental results show that the algorithm is more accurate and can converge steadily and rapidly, when original data is far from exact value.