Integrated celestial navigation for spacecraft using interferometer and Earth sensor

Xiong, Kai; Wei, Chunling

doi:10.1177/0954410020927522

Cited by 7 publications

(6 citation statements)

References 40 publications

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“…Besides, they also adopted the astronomical integrated navigation based on the ultraviolet earth sensor and optical interferometer, and introduced QLEKF to integrate the two measurement information to estimate the position, velocity, and attitude state error of the spacecraft 36 , and assess the optical path delay bias in the optical interferometer, and the proposed QLEKF demonstrates superior estimation performance compared to the conventional EKF. Nemati et al 37 adopted the RL to enhance the position estimation of the spacecraft and proposed the state estimation algorithm based on the combination of the RL and the Kalman filter to acquire the optimal solution of the state and observation noise covariance, which improved the PVSE accuracy for the spacecraft navigation.…”

Section: / 38mentioning

confidence: 99%

“…Compared with the above research [35][36][37][38] , the intelligent Q-learning algorithm used to tune the parameters of the state and observation noise covariance is also improved accordingly. The reward mechanism and Q-table of the reinforcement Q-learning is designed according to the sub-filter characteristics of the federated filter, and the iterative period of the Q-learning is taken as the algorithm to evaluate the cumulative reward.…”

Section: / 38mentioning

confidence: 99%

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Intelligent QLFEKF integrated navigation based on the X-ray pulsar / solar and target planetary Doppler for the SSBE cruise phase

Tao,

Song,

Lin

et al. 2024

Preprint

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The Solar System Boundary Exploration (SSBE) is the focal point for future far-reaching space exploration. Due to the SSBE having many scientific difficulties that need to be studied, such as super-long space exploratorydistance, super-long flighttime in orbit, and super-large communication data delay between the ground and the probe, the probe must have sufficient intelligence to realize intelligent autonomous navigation. The traditional navigation schemes have been unable to provide high accuracy autonomous intelligent navigation independent on the ground for the probe. Therefore, high accuracyintelligent astronomical integrated navigation would provide new methods and technologies for the navigation of the SSBE. The probe of the SSBE is disturbed by multiple sources of solar light pressure and the complex unknown environment during the long cruise operation in orbit. In order to ensure the position stateand velocity state error estimation high accuracy for the probe cruise phase, an autonomous intelligent integrated navigation scheme based on the X-ray pulsar / solar and target planetary Doppler velocity measurement is proposed. The reinforcement Q-learning method is introduced, and the reward mechanism is designed for trial-and-error tuning of stateand observationnoise error covariance parameters. The federated extended Kalman filter (FEKF) based on Q-learning (QLFEKF) navigation algorithm is proposed to achieve high accuracystate estimation of the autonomous intelligence for the SSBE cruise phase. The main advantage of the QLFEKF is that the Q-learning combined with the conventional federated filtering method could optimize the state parameters in real-time, and obtain high position and velocity state estimation (PVSE)accuracy. Compared with the conventional FEKF integrated navigation algorithm, the PVSEnavigation accuracy of the federated filter integrated based the Q-learning navigation algorithm are improved by 55.84% and 37.04% respectively, which present with the higher accuracy and greater capability of the raised autonomous intelligent integrated navigation algorithm. The simulation results show that the intelligent integrated navigation algorithm based on QLFEKF has higher accuracy navigation,and able to satisfy the demandsof autonomous high accuracy for the SSBE cruise phase.

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Section: / 38mentioning

confidence: 99%

Section: / 38mentioning

confidence: 99%

Intelligent QLFEKF integrated navigation based on the X-ray pulsar / solar and target planetary Doppler for the SSBE cruise phase

Tao,

Song,

Lin

et al. 2024

Preprint

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“…6 GNSS is an alternative solution that can give localization accurately. 7 However, due to the obstacles and interference phenomena, GPS signals can be blocked or attenuated (jamming). Hence, GPS will not provide data all the time.…”

Section: Overview and Related Workmentioning

confidence: 99%

Robust attitude estimation for an unmanned aerial vehicle using multiple GPS receivers

Dhahbane¹,

Nemra²,

Sakhi³

2022

Proceedings of the Institution of Mechanical Engineers, Part G:

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Estimation of the system state is an important task in the field of control, localization, and navigation. Many sensors are used to extract the attitude information. GPS (Global Positioning System) is widely used to provide position and velocity for ground and aerial vehicles. However, only a few research works have addressed the problem of attitude estimation using multiple GPS receivers. The main contribution of this paper consists on proposing a robust algorithm for attitude estimation of an unmanned aerial vehicle using INS/3GPS integration. First, we propose two alternative methods for attitude determination with three GPS receivers: Direct Computing Method (DCM) and Least Square Method (LSM) based on Singular Value Decomposition (SVD). Second, the attitude given by GPS receivers is fused with the measurement of a three-axis gyroscope using a robust Smooth Variable Structure Filter (SVSF). The optimal geometric configuration of GPS antennas is determined based on a deep performance analysis of the proposed system. Then, robustness analysis is assessed against measurement noise and parameters uncertainties. Simulation results are presented to demonstrate the robustness of the proposed approach. A more realistic evaluation of the proposed solution is obtained using an aerial vehicle type “Helicopter” from the Virtual Robot Experimentation Platform (VREP simulator). The proposed filter is compared with an Extended Kalman Filter (EKF). The obtained results have confirmed how much the SVSF is robust for attitude estimation in presence of the considered disturbances.

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“…[4][5][6][7] Several spacecraft autonomous navigation methods have been developed with different kinds of measurements, such as autonomous optical navigation (OP-NAV), [8][9][10] X-ray pulsar navigation (XNAV), [11][12][13][14] stellar spectrum-based velocimetry 15,16 and their integrated navigation schemes. 17,18 The basic idea of the autonomous optical navigation is to acquire the spacecraft's position and attitude using the optical images of the objects (such as stars, Sun, Earth, Moon, planet and asteroids) with on-board cameras. The OPNAV System has been applied for many space missions, including Deep Space 1, Stardust, Deep Impact, Hayabusa 2, 19 and Chang'E-5.…”

Section: Introductionmentioning

confidence: 99%

“…16 Recently, the StarNAV (Star Navigation) is developed for autonomous navigation for spacecraft based on the measurement of the change in inter-star angles due to relativistic perturbations, which are related to the spacecraft's velocity and position. 18,22 The StarNAV is a promising technique as both the star catalogs and the inter-star angle measurement are achievable with high precision.…”

Section: Introductionmentioning

confidence: 99%

Spacecraft autonomous navigation using line-of-sight directions of non-cooperative targets by improved Q-learning based extended Kalman filter

Xiong,

Zhou,

Wei

2023

Proceedings of the Institution of Mechanical Engineers, Part G:

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Autonomous optical navigation is one of the most promising techniques to estimate the position and velocity of a spacecraft in Earth orbit without the supports of Earth-based tracking stations. To improve the navigation performance, this paper presents a novel autonomous optical navigation method, where a star camera on the spacecraft is utilized to measure the line-of-sight (LOS) directions of a number of non-cooperative space targets, whose position vectors are supposed to be not precisely known in advance, and an improved Q-learning based extended Kalman filter (IQEKF) is developed to obtain the accurate motion state estimate of both the spacecraft and the space targets based on the LOS direction measurements. The main advantage of the presented method is that the LOS directions of the space targets can be acquired with high-accuracy by using the state-of-the-art star camera, such that the superior navigation accuracy is achievable. In addition, the whole motion state of the spacecraft, such as position, velocity, and attitude, can be obtained with the star camera, in the case that the space targets and the stars are observed simultaneously. The high performance of the presented autonomous navigation method is illustrated through a representative simulation of a medium Earth orbit (MEO) satellite. Furthermore, the simulation results indicate that the IQEKF yields more accurate solutions than the traditional navigation filtering algorithms.

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Integrated celestial navigation for spacecraft using interferometer and Earth sensor

Cited by 7 publications

References 40 publications

Intelligent QLFEKF integrated navigation based on the X-ray pulsar / solar and target planetary Doppler for the SSBE cruise phase

Intelligent QLFEKF integrated navigation based on the X-ray pulsar / solar and target planetary Doppler for the SSBE cruise phase

Robust attitude estimation for an unmanned aerial vehicle using multiple GPS receivers

Spacecraft autonomous navigation using line-of-sight directions of non-cooperative targets by improved Q-learning based extended Kalman filter

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