Motion prediction of an uncontrolled space target

Zhou, Bang-Zhao; Liu, Xiaofeng; Cai, Guo‐Ping; Liu, Yunmeng; Liu, Pan

doi:10.1016/j.asr.2018.09.025

Cited by 9 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where, I(K) has the definition as in Equation (7) and is a function of inertia ratio parameters K. Also, Ω(ω(k)) is given as…”

Section: Unscented Kalman Filtermentioning

confidence: 99%

“…Besides the EKF-based approaches for the parameter and motion estimation problem, researchers have also implemented an advanced Kalman Filter technique called the Unscented Kalman Filter (UKF) which does not require the linearization of the non-linear dynamics systems and is generally more robust to system nonlinearity than EKF. [7][8][9] Additionally, apart from the Kalman Filter related techniques, several other physics model-based approaches have been studied, which include the non-linear least square technique, 10 smoothing and mapping factor-graph-based method, 11 constrained convex optimization method, 12 etc.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Advanced motion estimations and predictions of a tumbling, non-cooperative space object during long-term occlusion

Kabir,

Bai

2024

Sensors and Systems for Space Applications XVII

View full text Add to dashboard Cite

This study aims to improve the rotational motion and inertia parameters estimation performance of an Unscented Kalman Filter model (UKF) for a torque-free tumbling non-cooperative space object using the Gaussian Process (GP). The traditional UKF algorithm which is a physics-based estimation algorithm for non-linear systems is susceptible to the physical process, measurement sampling rate, and filter design. Consequently, slight inaccuracy in the assumed physical models, low sampling rates, or small variations of the filter parameters can result in poor estimation performance. Additionally, the UKF model might not predict the motion and inertia parameters with good accuracy in the absence of sensor measurements, also known as occlusion, a quite common challenge for space missions. To make a UKF model more robust to the factors above, we utilize multi-output GP models with periodic kernels to make long-term predictions of the position and attitude measurements obtained from a Laser Camera System (LCS). These measurement predictions from GP models are used as the sensor measurements for the UKF model. We implement a Fast Fourier Transform on the sensor measurements to determine the initial guess for periodicity hyper-parameters for the periodic kernels. Results from conducted simulations show that the proposed UKF model with GP-predicted measurements (UKF-GP model) performs remarkably well compared to the UKF model under the assumption of long-term occlusion. It is also observed from the results that, the UKF-GP model is more robust to sensor sampling rate, underlying physical process, and filter parameters even with occlusion, compared to the UKF model without occlusion.

show abstract

“…where, I(K) has the definition as in Equation (7) and is a function of inertia ratio parameters K. Also, Ω(ω(k)) is given as…”

Section: Unscented Kalman Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Advanced motion estimations and predictions of a tumbling, non-cooperative space object during long-term occlusion

Kabir,

Bai

2024

Sensors and Systems for Space Applications XVII

View full text Add to dashboard Cite

show abstract

“…The term uncooperative refers to those operations in which there is not communication with the target spacecraft or object, and even in some cases without auxiliar markers which can be used for a raw pose (position and rotation) determination. Therefore, the pose determination of the target is needed to perform the operation successfully (Lampariello et al, 2021;Zhou et al, 2019). Two key factors of these positioning systems are the use of spacecraft resources (power consumption, voltage, etc.)…”

Section: Introductionmentioning

confidence: 99%

Relative Pose Determination Algorithm for Space on-Orbit Close Range Autonomous Operations Using Lidar

Castro-Traba,

Fontenla-Carrera,

González-deSantos

et al. 2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. Non cooperative on-orbit operations, such as rendezvous, docking or berthing operations, have become more relevant, mainly due to the necessity of expanding mission lifetimes, the increase of space debris and the reduction of human dependency. In order to automate these operations, the relative pose calculation between the target and the chaser must be determined autonomously. In recent years, LiDAR sensors have been introduced for this problem, achieving good accuracies. The critical part of this operation is the first relative pose calculation, since there is no previous information about the attitude of the target. In this work, a methodology to carry out this first relative pose calculation using LiDAR sensors is presented. A template matching algorithm has been developed, which uses the 3D model of the target to calculate the relative pose of the target regarding the LiDAR sensor. Three different study cases, with different distances and rotations, have been simulated in order to validate the algorithm, reaching an average error of 0.0383m.

show abstract