# Learning Generative Models for Climbing Aircraft from Radar Data

arXiv: https://arxiv.org/abs/2309.14941 · HTML: https://mlsanity.com/paper/2309.14941/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 40 references that are in the map.
- First posted on arXiv 26 Sep 2023.

## Builds on (most cited first, 3 of 3)

- [NOMU: Neural Optimization-based Model Uncertainty](https://mlsanity.com/paper/2102.13640/) (2021)
- [4D flight trajectory prediction using a hybrid Deep Learning prediction method based on ADS-B technology: a case study of Hartsfield-Jackson Atlanta International Airport(ATL)](https://mlsanity.com/paper/2110.07774/) (2021)
- [A Probabilistic Model for Aircraft in Climb using Monotonic Functional Gaussian Process Emulators](https://mlsanity.com/paper/2210.01445/) (2022)

## Built on by (most cited first, 3 of 3)

- [MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2407.16312/) (2024)
- [AirTrafficGen: Configurable Air Traffic Scenario Generation with Large Language Models](https://mlsanity.com/paper/2508.02269/) (2025)
- [A framework for assuring the accuracy and fidelity of an AI-enabled Digital Twin of en route UK airspace](https://mlsanity.com/paper/2601.03120/) (2026)

## Abstract (arXiv)

Accurate trajectory prediction (TP) for climbing aircraft is hampered by the presence of epistemic uncertainties concerning aircraft operation, which can lead to significant misspecification between predicted and observed trajectories. This paper proposes a generative model for climbing aircraft in which the standard Base of Aircraft Data (BADA) model is enriched by a functional correction to the thrust that is learned from data. The method offers three features: predictions of the arrival time with 26.7% less error when compared to BADA; generated trajectories that are realistic when compared to test data; and a means of computing confidence bounds for minimal computational cost.