A Novel Probability Weighting Method To Fit Gaussian Functions

TL;DR

This paper introduces a new probability weighting algorithm for fitting Gaussian functions, improving precision and robustness by incorporating confidence-based weighting, with promising simulation results.

Contribution

It presents a novel weighting method for Gaussian fitting that enhances accuracy and robustness compared to existing techniques.

Findings

Demonstrates improved fitting precision

Shows increased robustness in simulations

Outperforms comparable methods in tests

Abstract

Gaussian functions are commonly used in different fields, many real signals can be modeled into such form. Research aiming to obtain a precise fitting result for these functions is very meaningful. This manuscript intends to introduce a new algorithm used to estimate the full parameters of the Gaussian-shaped function. It is basically a weighting method, starting from Caruana's method, while the selection of weighting factors is from the statistics view and based on the estimation of the confidence level for the samples. Tests designed for comparison with current similar methods have been conducted. The simulation results indicate a good performance for this new method, mainly in precision and robustness.