Unscented Kalman Filtering for Relative Spacecraft Attitude and Position Estimation

Zhang, Lijun; Li, Tong; Yang, Huabo; Zhang, Shifeng; Cai, Hong; Qian, Shengsheng

doi:10.1017/s0373463314000769

Cited by 7 publications

(7 citation statements)

References 24 publications

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“…Navigation systems for close-proximity operations have been extensively validated in the context of RF and monocular vision navigation for FF and on-orbit servicing, when the target is cooperative [64,1,65,61,66]. However, there is still a lack of a comprehensive validation of navigation systems for the pose estimation of an uncooperative target.…”

Section: Visual-based Navigation Filtersmentioning

confidence: 99%

“…However, there is still a lack of a comprehensive validation of navigation systems for the pose estimation of an uncooperative target. As an example, the EKF and the Unscented Kalman Filter (UKF) presented in [61] and [66], respectively, rely on the availability of gyro measurements from each spacecraft, which is usually not the case for uncooperative spacecraft in ADR scenarios. When the uncooperative target is known, it is assumed that a simplified geometrical model of the target is available and representative of the target state in orbit.…”

Section: Visual-based Navigation Filtersmentioning

confidence: 99%

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Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft

Cassinis

Fonod

Gill

2019

Progress in Aerospace Sciences

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The relative pose estimation of an inactive target by an active servicer spacecraft is a critical task in the design of current and planned space missions, due to its relevance for close-proximity operations, i.e. the rendezvous with a space debris and/or in-orbit servicing. Pose estimation systems based solely on a monocular camera are recently becoming an attractive alternative to systems based on active sensors or stereo cameras, due to their reduced mass, power consumption and system complexity. In this framework, a review of the robustness and applicability of monocular systems for the pose estimation of an uncooperative spacecraft is provided. Special focus is put on the advantages of multispectral monocular systems as well as on the improved robustness of novel image processing schemes and pose estimation solvers. The limitations and drawbacks of the validation of current pose estimation schemes with synthetic images are further discussed, together with the critical trade-offs for the selection of visual-based navigation filters. The state-of-the-art techniques are analyzed in order to provide an insight into the limitations involved under adverse illumination and orbit scenarios, high image contrast, background noise, and low signal-to-noise ratio, which characterize actual space imagery, and which could jeopardize the image processing algorithms and affect the pose estimation accuracy as well as the navigation filter's robustness. Specifically, a comparative

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Section: Visual-based Navigation Filtersmentioning

confidence: 99%

Section: Visual-based Navigation Filtersmentioning

confidence: 99%

Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft

Cassinis

Fonod

Gill

2019

Progress in Aerospace Sciences

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“…The observation vector is constructed as where dg and du are elements of the Euler angle deviation vector between the accelerometer and update and dj is the heading residual between the magnetometer and update. 17 The dynamic and observation models are expressed as…”

Section: Implementation Of Linear Kalman Filtermentioning

confidence: 99%

“…5 However, the method requires much computational time and still results in linearization errors compared with the regular Kalman filter. 17 The Kalman filter based on the DCM method avoids singularity and nonlinearity and exerts a better effect than EKF based on Euler and quaternion. 16 However, the method complicates the calculation because nine parameters, which are orthogonally constrained, need to be identified.…”

Section: Introductionmentioning

confidence: 99%

Novel method to integrate MARG and an odometer into AHRS for moving vehicles

Wang

Liu

2017

Advances in Mechanical Engineering

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To improve the performance of attitude and heading reference systems for moving vehicles, an effective orientation estimation method is proposed. The method involves the use of a low-cost magnetic, angular rate, and gravity sensor and an odometer. This study addresses the problems of large and abrupt changes in acceleration by moving vehicles and the issue of obtaining precise, long-term, drift-free attitude estimates. A low-pass filter is used to eliminate the highfrequency part of linear acceleration. An improved method to evaluate motion status is proposed and utilized to compensate for filtered accelerometer measurement when the vehicle is accelerating or steering. The proposed method uses an effective adaptive Kalman filter based on the error state model to reduce dynamic perturbations. Filter gain is adaptively tuned under different moving modes by adjusting the noise matrix. Experiments are performed to validate the algorithms under realistic motions, and simulations are implemented to examine their performance.

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“…Kalman lter in estimating the state of Spacecraft Formation Flying, an alternative ltering approach has been developed based on unscented transformation principles. The UKF has been proposed for relative spacecraft attitude and position estimation [Zhang et al 2015].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Attitude and Formation Estimation Spacecraft And Multi-Agent Systems

Rososhansky¹

2021

Preprint

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This dissertation examines the state and parameter estimation problem of monolithic spacecraft and multi-agent systems in conjunction with the control algorithms. Nonlinear filtering techniques are investigated and applied to the problems of attitude estimation and control of monolithic spacecraft, distributed flltering for attitude estimation and control of satellite formation flying (SFF), and estimation and control of a multi-agent system in consensus tracking with uncertain dynamic model. The main objective is to investigate the performance of nonlinear filtering techniques under fault-free and fault-prone scenarios. In essence, the core of this research has been placed on identifying techniques to improve the efficiency and reduce the variance of estimations in nonlinear filtering. The research is primarily dedicated to the investigation of adaptive unscented Kalman Filter (AUKF) and particle Filter (PF). A nonlinear filtering technique has been proposed for sequential joint estimation of a multi-agent system in consensus tracking with uncertain dynamic model. The new filter is called marginalized unscented particle Filter (MUPF). The proposed filter uses the Rao-Blackwellised principle to couple the particle filtering technique with unscented transform algorithm

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Unscented Kalman Filtering for Relative Spacecraft Attitude and Position Estimation

Cited by 7 publications

References 24 publications

Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft

Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft

Novel method to integrate MARG and an odometer into AHRS for moving vehicles

Nonlinear Attitude and Formation Estimation Spacecraft And Multi-Agent Systems

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