A statistical analysis of least-squares circle-centre estimation

We examine the problem of fitting a circle to a set of noisy measurement of points on the circle´s circumference. An estimator based on the standard least-squares techniques has been proposed by Delogne which has been shown by Kasa to be convenient for its ease of analysis and computation. Using Chan´s circular functional model to describe the distribution of points, we perform a statistical analysis of the circle´s centre estimation, assuming an independent and identical distributed Gaussian measurement errors. We examine the existence of the mean and variance of the estimator for fixed sample sizes. We find that the mean exists when the number of sample points is greater than 2 and the variance exists when this number is greater than 3. We also derive the approximations for the mean and variance for fixed sample sizes when the noise variance is small. We find that the bias approaches zero as the noise variance diminishes and that the variance approaches the Cramer-Rao lower bound. We show this through Monte-Carlo simulations.