Spacecraft autonomous navigation using unscented particle filter-based celestial/Doppler information fusion

Ning, Xiaolin; Fang, Jiancheng

doi:10.1088/0957-0233/19/9/095203

Cited by 49 publications

(33 citation statements)

References 31 publications

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“…Thus, with regards to the known model of the ephemeris error and the real time requirement, KF is the most suitable method for estimating ephemeris uncertainty. Ning and Fang (2008) provide an information fusion method for Earth satellites to effectively combine the Doppler and celestial measurements. However, the study ignores the influence of the ephemeris uncertainty.…”

Section: I N M a A N D O T H E R S Vol 69mentioning

confidence: 99%

“…However, the study ignores the influence of the ephemeris uncertainty. Motivated by Ning and Fang (2008), this paper proposes a Radio/Optical information fusion method based on estimation and correction of the target ephemeris error. The main contribution of this paper is:…”

Section: I N M a A N D O T H E R S Vol 69mentioning

confidence: 99%

“…Coordinates Transformation). The global optimal estimation and its error covariance X g and P g in the TCI system in the master filter is obtained by the following equations (Ning and Fang, 2008):…”

Section: I N M a A N D O T H E R Smentioning

confidence: 99%

See 2 more Smart Citations

A Radio/Optical Integrated Navigation Method Based on Ephemeris Correction for an Interplanetary Probe to approach a Target Planet

Fang

Ning

et al. 2015

J. Navigation

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To obtain accurate navigation results with respect to Earth simultaneously with those with respect to the target for an interplanetary probe to approach the target planet, this paper proposes a Radio/Optical integrated navigation method based on ephemeris correction, which deeply affects the fusion accuracy. In this paper, the model of the ephemeris error is established, and taking the analytical solution of the ephemeris uncertainty as measurement, the target ephemeris error and its covariance are estimated by Kalman filter and fed back to modify the force models. By correcting the target ephemeris and using information fusion, the Radio/Optical integrated navigation prevents the ephemeris uncertainty polluting the fusion accuracy, and efficiently combines the radio and optical navigation results. The results show the influence of the ephemeris error can be removed, and the Radio/Optical integrated navigation is capable of providing accurate navigation results with respect to Earth and the target. The results demonstrate the proposed method yields an accuracy superior to the conventional method, which proves its effectiveness.2. Optical navigation. 3. Radio navigation. 4. Ephemeris error estimation.

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Section: I N M a A N D O T H E R S Vol 69mentioning

confidence: 99%

Section: I N M a A N D O T H E R S Vol 69mentioning

confidence: 99%

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A Radio/Optical Integrated Navigation Method Based on Ephemeris Correction for an Interplanetary Probe to approach a Target Planet

Fang

Ning

et al. 2015

J. Navigation

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“…However, the need to deal with non-linearity and non-Gaussianity conditions, which hold for example in low-fuel missions where nonlinearities are exploited to reduce fuel consumption (Grover and Sato, 2012), have pushed researchers to investigate the use of advance filters e.g. particle filters for space navigation (Ning and Fang, 2008), (Ke et al, 2013).…”

Section: Review Of Space Navigation: Sensors Systems and Algorithmsmentioning

confidence: 99%

The Perigeo Project: Inertial and Imaging Sensors Processing, Integration and Validation on Uav Platforms for Space Navigation

Molina

Angelats²,

Colomina³

et al. 2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The PERIGEO R&D project aims at developing, testing and validating algorithms and/or methods for space missions in various field of research. This paper focuses in one of the scenarios considered in PERIGEO: navigation for atmospheric flights. Space missions heavily rely on navigation to reach success, and autonomy of on-board navigation systems and sensors is desired to reach new frontiers of space exploration. From the technology side, optical frame cameras, LiDAR and inertial technologies are selected to cover the requirements of such missions. From the processing side, image processing techniques are developed for vision-based relative and absolute navigation, based on point extraction and matching from camera images, and crater detection and matching in camera and LiDAR images. The current paper addresses the challenges of space navigation, presents the current developments and preliminary results, and describes payload elements to be integrated in an Unmanned Aerial Vehicle (UAV) for in-flight testing of systems and algorithms. Again, UAVs are key enablers of scientific capabilities, in this case, to bridge the gap between laboratory simulation and expensive, real space missions.

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“…PF have appeared in recent years (Ning and Fang, 2008;Li et al, 2012;Yang et al, 2014). In order to improve the performance of UKF in case of uncertain noise statistic characteristics, a noise statistic estimator was designed to estimate uncertain noise characteristics online for UKF by Cheng et al (2014).…”

mentioning

confidence: 99%

Spacecraft Autonomous GPS Navigation based on Polytopic Linear Differential Inclusion

Chen

Liu

2014

J. Navigation

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Aiming at improving the poor real-time performance of existing nonlinear filtering algorithms applied to spacecraft autonomous navigation based on Global Positioning System (GPS) measurements and simplifying the algorithm design of navigation algorithms, a spacecraft autonomous navigation algorithm based on polytopic linear differential inclusion is proposed in this paper. Firstly, it is demonstrated that the nonlinear estimation error system of spacecraft autonomous navigation can be modelled as a polytopic linear differential inclusion system model according to the idea of global linearization. Thus, the filtering of a nonlinear system simplified to a filtering of a polytopic linear system with coefficients. Secondly, Tensor-Product (TP) model transformation is applied to determine the polytopic linear differential inclusion system model. The model error introduced by global linearization is reduced and the compromise between computational complexity and modelling accuracy is realised. Finally, a spacecraft autonomous navigation algorithm based on polytopic linear differential inclusion is designed by combining multi-model Kalman filtering with data fusion. Compared with an Extended Kalman Filter (EKF), the proposed algorithm is simpler and easier to implement since it need not update the Jacobian matrices online. Simulation results demonstrate the same estimation accuracy of the proposed algorithm to that of EKF.2. Polytopic linear differential inclusion. 3. TP model transformation.4. Multiple models Kalman filtering. 5. Data fusion.

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Spacecraft autonomous navigation using unscented particle filter-based celestial/Doppler information fusion

Cited by 49 publications

References 31 publications

A Radio/Optical Integrated Navigation Method Based on Ephemeris Correction for an Interplanetary Probe to approach a Target Planet

A Radio/Optical Integrated Navigation Method Based on Ephemeris Correction for an Interplanetary Probe to approach a Target Planet

The Perigeo Project: Inertial and Imaging Sensors Processing, Integration and Validation on Uav Platforms for Space Navigation

Spacecraft Autonomous GPS Navigation based on Polytopic Linear Differential Inclusion

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