Details

Autor(en) / Beteiligte

• Vida, Denis
• Gural, Peter S
• Brown, Peter G
• Campbell-Brown, Margaret
• Wiegert, Paul

Titel

Estimating trajectories of meteors: an observational Monte Carlo approach – I. Theory

Ist Teil von

• Monthly notices of the Royal Astronomical Society, 2020-01, Vol.491 (2), p.2688-2705

Erscheinungsjahr

2020

Link zum Volltext

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• ABSTRACT It has recently been shown by Egal et al. that some types of existing meteor in-atmosphere trajectory estimation methods may be less accurate than others, particularly when applied to high-precision optical measurements. The comparative performance of trajectory solution methods has previously only been examined for a small number of cases. Besides the radiant, orbital accuracy depends on the estimation of pre-atmosphere velocities, which have both random and systematic biases. Thus, it is critical to understand the uncertainty in velocity measurement inherent to each trajectory estimation method. In this first of a series of two papers, we introduce a novel meteor trajectory estimation method that uses the observed dynamics of meteors across stations as a global optimization function and that does not require either a theoretical or an empirical flight model to solve for velocity. We also develop a 3D observational meteor trajectory simulator that uses a meteor ablation model to replicate the dynamics of meteoroid flight, as a means to validate different trajectory solvers. We both test this new method and compare it to other methods, using synthetic meteors from three major showers spanning a wide range of velocities and geometries (Draconids, Geminids, and Perseids). We determine which meteor trajectory solving algorithm performs better for all-sky, moderate field-of-view, and high-precision narrow-field optical meteor detection systems. The results are presented in the second paper in this series. Finally, we give detailed equations for estimating meteor trajectories and analytically computing meteoroid orbits, and provide the python code of the methodology as open-source software.