Analysing Meteoroid Flights Using Particle Filters

TL;DR

This paper introduces a particle filter method for modeling meteoroid trajectories from fireball observations, providing a probabilistic framework that accounts for uncertainties without requiring predefined initial conditions.

Contribution

It presents a novel application of particle filters to meteoroid trajectory estimation, improving uncertainty modeling and automation over traditional methods.

Findings

Accurately estimated final mass and velocity of the fireball

Demonstrated effectiveness of particle filters in trajectory modeling

Enabled potential for unbiased analysis of fireball data

Abstract

Fireball observations from camera networks provide position and time information along the trajectory of a meteoroid that is transiting our atmosphere. The complete dynamical state of the meteoroid at each measured time can be estimated using Bayesian filtering techniques. A particle filter is a novel approach to modelling the uncertainty in meteoroid trajectories and incorporates errors in initial parameters, the dynamical model used and observed position measurements. Unlike other stochastic approaches, a particle filter does not require predefined values for initial conditions or unobservable trajectory parameters. The Bunburra Rockhole fireball (Spurn\'y et al. 2012), observed by the Australian Desert Fireball Network (DFN) in 2007, is used to determine the effectiveness of a particle filter for use in fireball trajectory modelling. The final mass is determined to be $2.16\pm1.33\, kg$ with a final velocity of $6030\pm216\, m\,s^{-1}$, similar to previously calculated values. The full automatability of this approach will allow an unbiased evaluation of all events observed by the DFN and lead to a better understanding of the dynamical state and size frequency distribution of asteroid and cometary debris in the inner solar system.