Comparative analysis of Neural Networks approaches for Initial Orbit Determination of Near-Earth Objects
Year: 2026
Type: preprint
Abstract: Initial Orbit Determination (IOD) from Very Short Arcs (VSAs) remains one of the most challenging open problems in asteroid surveillance and celestial mechanics. Classical methods require angular observations spanning a sufficient fraction of the orbit, and become ill-conditioned or fail outright when only a single observing night is available, as is the case for most newly discovered Near-Earth Objects (NEOs). We present a comparative analysis of two Neural Network (NN) models that attack the ranging problem directly: both ingest a triplet of time-tagged angular measurements $(t_i,α_i,δ_i)$, $i=1,2,3$, from a single VSA and predict the geocentric range and range-rate $(ρ,\dotρ)$, thus completing the orbital state vector. The first is a Multi-Layer Perceptron trained on a purely data-driven objective; the second augments it with a physics-informed loss. Both are trained, under an object-disjoint partition, on a sample of the $568\,127$ VSAs of $39\,031$ real NEOs available in the NEODyS-2 catalogue. Each model returns an estimate on every arc by construction, so we assess instead whether that estimate is dynamically admissible: on a held-out test set of $56\,773$ arcs the physics-informed model places $85.1\%$ of its predictions inside the admissible region, against $76.5\%$ for the data-driven baseline, whereas Gauss's and Laplace's methods return a solution on only $45.0\%$ and $47.3\%$ of the same arcs and collapse onto the degenerate root in about nine of those cases out of ten. A stratified analysis over proper motion and true range shows that the physics-informed objective is not uniformly superior to the baseline: it trades a longer error tail for admissibility and for a marked advantage on the fast, nearby arcs that are operationally the most relevant.
Source: arXiv (Cornell University)
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7213892035