Representation–Propagation Learning for Closed-loop Error-Controlled Long-Horizon Aircraft Trajectory Forecasting

Long-horizon aircraft trajectory forecasting is challenging because uncertainty in future flight intent can be further amplified during physical rollout. We propose Representation–Propagation Learning (RPL), a forecasting framework that explicitly separates future-intent representation from trajectory propagation. RPL first infers a latent future intent and decodes it into a reference trajectory, which is then realized through differentiable aircraft dynamics with bounded closed-loop feedback. Our main theoretical result characterizes the sensitivity of this propagation process. By separating feedback-stabilized tracking variables from the unavoidable position integrators, we show that, under finite-gain closed-loop tracking dynamics, perturbations in future intent induce a terminal-state sensitivity that grows at most as O( √ T) with the forecast horizon, rather than exponentially. This provides a theoretical basis for controlling error amplification in long-horizon physical rollout without requiring contraction of the complete aircraft state. Experiments conducted on 21,816 held-out ADS-B forecasting windows demonstrate that RPL improves long-horizon forecasting accuracy and disturbance robustness in our evaluation. Although CFM performs better over short horizons, RPL’s advantages become apparent as the prediction horizon extends; at a 120-second prediction horizon, RPL reduces ADE and FDE by 9.79% and 5.17%, respectively, compared with CFM. Controlled perturbation experiments further show that the closed-loop feedback mechanism substantially reduces error accumulation and performance degradation during rollout, supporting RPL’s robustness in long-horizon trajectory prediction. You can get the code from this anonymous repository.