Adaptive Neural Network-based Unscented Kalman Filter for Spacecraft Pose Tracking at Rendezvous

06/08/2022
by   Tae Ha Park, et al.
0

This paper presents a neural network-based Unscented Kalman Filter (UKF) to track the pose (i.e., position and orientation) of a known, noncooperative, tumbling target spacecraft in a close-proximity rendezvous scenario. The UKF estimates the relative orbital and attitude states of the target with respect to the servicer based on the pose information extracted from incoming monocular images of the target spacecraft with a Convolutional Neural Network (CNN). In order to enable reliable tracking, the process noise covariance matrix of the UKF is tuned online using adaptive state noise compensation. Specifically, the closed-form process noise model for the relative attitude dynamics is newly derived and implemented. In order to enable a comprehensive analysis of the performance and robustness of the proposed CNN-powered UKF, this paper also introduces the Satellite Hardware-In-the-loop Rendezvous Trajectories (SHIRT) dataset which comprises the labeled imagery of two representative rendezvous trajectories in low Earth orbit. For each trajectory, two sets of images are respectively created from a graphics renderer and a robotic testbed to allow testing the filter's robustness across domain gap. The proposed UKF is evaluated on both domains of the trajectories in SHIRT and is shown to have sub-decimeter-level position and degree-level orientation errors at steady-state.

READ FULL TEXT

page 3

page 13

page 14

research
09/20/2023

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

This work presents an Online Supervised Training (OST) method to enable ...
research
07/15/2020

Lunar Terrain Relative Navigation Using a Convolutional Neural Network for Visual Crater Detection

Terrain relative navigation can improve the precision of a spacecraft's ...
research
06/10/2023

Sliding Window Neural Generated Tracking Based on Measurement Model

In the pursuit of further advancement in the field of target tracking, t...
research
08/12/2021

Robotic Testbed for Rendezvous and Optical Navigation: Multi-Source Calibration and Machine Learning Use Cases

This work presents the most recent advances of the Robotic Testbed for R...
research
08/31/2020

Accurate Prediction and Estimation of 3D-Repetitive-Trajectories using Kalman Filter, Machine Learning and Curve-Fitting Method

Accurate estimation and prediction of trajectory is essential for the ca...
research
10/31/2017

Deep Forward and Inverse Perceptual Models for Tracking and Prediction

We consider the problems of learning forward models that map state to hi...
research
12/16/2020

MSL-RAPTOR: A 6DoF Relative Pose Tracker for Onboard Robotic Perception

Determining the relative position and orientation of objects in an envir...

Please sign up or login with your details

Forgot password? Click here to reset