Open Access

ARTICLE

Observability Analysis in Parameters Estimation of an Uncooperative Space Target

by Xianghao Hou, Gang Qiao

1 Acoustic Science and Technology Laboratory and College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin, 150001, China.

* Corresponding Author: Xianghao Hou. Email: .

(This article belongs to the Special Issue: Nonlinear Computational and Control Methods in Aerospace Engineering)

Computer Modeling in Engineering & Sciences 2020, 122(1), 175-205. https://doi.org/10.32604/cmes.2020.08452

Received 26 August 2019; Accepted 16 September 2019; Issue published 01 January 2020

Abstract

To study the parameter estimating effects of a free-floating tumbling space target, the extended Kalman filter (EKF) scheme is utilized with different high-nonlinear translational and rotational coupled kinematic & dynamic models on the LIDAR measurements. Applying the aforementioned models and measurements results in the situation where one single state can be estimated differently with varying accuracies since the EKFs based on different models have different observabilities. In the proposed EKFs, the traditional quaternions based kinematics and dynamics and the dual vector quaternions (DVQ) based kinematics and dynamics are used for the modeling of the relative motions between a chaser satellite and an uncooperative target. In the non-contact estimating scenarios, only highly nonlinear relative attitude and range measurements: the grapple fixture on the target measured from the chaser satellite via vision-based sensors, can be used. By evaluating the results of the EKFs, the observability properties of each EKF are studied analytically and numerically with the the Observability Gramian matrices (OG) and the standard deviations for every estimated parameters. The analysis of observability perform intensive studies and reveal the intrinsic factors that affect the accuracy and stability of the parameters estimation of an uncooperative space target. Finally, the analytical and numerical results show the optimal composition of the kinematic & dynamic models and measurements.

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Keywords

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