Papers › A Dynamic Trajectory Fit to Multi-Sensor Fireball Observations
A Dynamic Trajectory Fit to Multi-Sensor Fireball Observations
Trent Jansen-Sturgeon, Eleanor K. Sansom, Hadrien A. R. Devillepoix, Philip A. Bland, Martin C. Towner, Robert M. Howie, Benjamin A. D. Hartig
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Meteorites with known orbital origins are key to our understanding of Solar System formation and the source of life on Earth. However, these pristine samples of space material are incredibly rare. Less than 40 of the 60,000 meteorites held in collections around the world have known dynamical origins. Fireball networks have been developed globally in a unified effort to increase this number by using multiple observatories to record, triangulate, and dynamically analyse ablating meteoroids as they enter our atmosphere. The accuracy of the chosen meteoroid triangulation method directly influences the accuracy of the determined orbit and the likelihood of possible meteorite recovery. There are three leading techniques for meteoroid triangulation discussed in the literature: the Method of Planes, the Straight Line Least Squares method, and the Multi-Parameter Fit method. Here we describe an alternative method to meteoroid triangulation, called the Dynamic Trajectory Fit. This approach uses the meteoroid's 3D dynamic equations of motion to fit a realistic trajectory directly to multi-sensor line-of-sight observations. This method has the ability to resolve fragmentation events, fit systematic observatory timing offsets, and determine mass estimates of the meteoroid along its observable trajectory. Through a comprehensive Monte-Carlo analysis of over 100,000 trajectory simulations, we find this new method to more accurately estimate meteoroid trajectories of slow entry events ($<25 km/s) and events observed from low convergence angles (<$10$^{\circ}$) compared to existing meteoroid triangulation techniques. Additionally, we triangulate an observed fireball event with visible fragmentation using the various triangulation methods to show that the proposed Dynamic Trajectory Fit implementing fragmentation to best match the captured multi-sensor line-of-sight data.
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