Fresnel Integral Computation Techniques
Alexandru Ionut, James C. Hateley

TL;DR
This paper presents a comprehensive method for accurately and efficiently computing Fresnel Integrals across different argument ranges using Taylor series, trapezoid rule, and asymptotic expansion, with error estimates and parameter selection guidance.
Contribution
It introduces a systematic approach for choosing computation parameters for arbitrary precision in Fresnel Integral evaluation, extending previous methods.
Findings
Accurate computation of Fresnel Integrals across all argument ranges.
Provision of error estimates and parameter selection guidelines.
Numerical verification of the method's effectiveness with double precision.
Abstract
This work is an extension of previous work by Alazah et al. [M. Alazah, S. N. Chandler-Wilde, and S. La Porte, Numerische Mathematik, 128(4):635-661, 2014]. We split the computation of the Fresnel Integrals into 3 cases: a truncated Taylor series, modified trapezoid rule and an asymptotic expansion for small, medium and large arguments respectively. These special functions can be computed accurately and efficiently up to an arbitrary precision. Error estimates are provided and we give a systematic method in choosing the various parameters for a desired precision. We illustrate this method and verify numerically using double precision.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Vision and Imaging · Computer Graphics and Visualization Techniques · Robotics and Sensor-Based Localization
