# Online Supervised Training of Spaceborne Vision during Proximity Operations using Adaptive Kalman Filtering

arXiv: https://arxiv.org/abs/2309.11645 · HTML: https://mlsanity.com/paper/2309.11645/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2023.

## Built on by (most cited first, 5 of 5)

- [Bridging the Domain Gap for Flight-Ready Spaceborne Vision](https://mlsanity.com/paper/2409.11661/) (2024)
- [Improved 3D Gaussian Splatting of Unknown Spacecraft Structure Using Space Environment Illumination Knowledge](https://mlsanity.com/paper/2512.23998/) (2025)
- [Mixing Data-driven and Geometric Models for Satellite Docking Port State Estimation using an RGB or Event Camera](https://mlsanity.com/paper/2409.15581/) (2024)
- [Nonlinear Dynamical Systems for Automatic Face Annotation in Head Tracking and Pose Estimation](https://mlsanity.com/paper/2502.15179/) (2025)
- [Robust Surgical Robotic Instrument Tracking via Sequential Multi-Cue Fusion and Sim-to-Real Self-Training](https://mlsanity.com/paper/2610.05491/) (2026)

## Abstract (arXiv)

This work presents an Online Supervised Training (OST) method to enable robust vision-based navigation about a non-cooperative spacecraft. Spaceborne Neural Networks (NN) are susceptible to domain gap as they are primarily trained with synthetic images due to the inaccessibility of space. OST aims to close this gap by training a pose estimation NN online using incoming flight images during Rendezvous and Proximity Operations (RPO). The pseudo-labels are provided by adaptive unscented Kalman filter where the NN is used in the loop as a measurement module. Specifically, the filter tracks the target's relative orbital and attitude motion, and its accuracy is ensured by robust on-ground training of the NN using only synthetic data. The experiments on real hardware-in-the-loop trajectory images show that OST can improve the NN performance on the target image domain given that OST is performed on images of the target viewed from a diverse set of directions during RPO.