Calibration of Satellite Autonomous Navigation Based on Attitude Sensor

Jiansong, Chang; Geng, Yunhai; Guo, Jianxin; Fan, Wei

doi:10.2514/1.g000684

Cited by 8 publications

(7 citation statements)

References 7 publications

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“…Combining the distance and the LOS direction information produces a measurement of the spacecraft position vector rk with moderate accuracy. Considering that Earth is often described as a triaxial ellipsoid, the interested readers are pointed to literature 22–24 for details about ellipse-fitting algorithm and Earth oblateness compensation.…”

Section: Spacecraft Position Estimation Methodsmentioning

confidence: 99%

Integrated celestial navigation for spacecraft using interferometer and Earth sensor

Xiong¹,

Wei²

2020

Proceedings of the Institution of Mechanical Engineers, Part G:

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An integrated celestial navigation scheme for spacecrafts based on an optical interferometer and an ultraviolet Earth sensor is presented in this paper. The optical interferometer is adopted to measure the change in inter-star angles due to stellar aberration, which provides information on the velocity of the spacecraft in the plane perpendicular to the direction of the observed star. In order to enhance the navigation performance, the measurements obtained from the ultraviolet Earth sensor is used to eliminate the unfavorable effect caused by the gravitational deflection of starlight. As the prior knowledge about the optical path delay bias of the optical interferometer may be ambiguous, a Q-learning extended Kalman filter is derived to fuse the two types of measurements, and estimate the kinematic state together with the optical path delay bias. The solution of the autonomous navigation system consists of position, velocity and attitude of the spacecraft. Numerical simulation shows that an evident improvement in navigation accuracy can be achieved by introducing the ultraviolet Earth sensor into the navigation system. In addition, it is shown that the Q-learning extended Kalman filter performs better than the traditional extended Kalman filter.

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Section: Spacecraft Position Estimation Methodsmentioning

confidence: 99%

Integrated celestial navigation for spacecraft using interferometer and Earth sensor

Xiong¹,

Wei²

2020

Proceedings of the Institution of Mechanical Engineers, Part G:

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“…In addition, the model of the considered autonomous navigation system is nonlinear. The linearization approach, which is similar to the extended Kalman filter (EKF), 7 is available for the covariance propagation of the PMAKF.…”

Section: Autonomous Navigation For Constellationmentioning

confidence: 99%

“…In recent years, autonomous navigation is extensively studied and it is seen as a key technology of the new generation global navigation satellite system (GNSS). [1][2][3][4][5][6][7][8] Autonomous navigation of a satellite constellation is the process by which the position parameters of the satellites are autonomously estimated using the onboard measuring devices and without the supports of Earth-based tracking stations. With the application of this technique, the survivability of the constellation system will be enhanced, and the cost for tracking satellites will be reduced.…”

Section: Introductionmentioning

confidence: 99%

Parallel model adaptive Kalman filtering for autonomous navigation with line-of-sight measurements

Xiong¹,

Wei²,

Zhang³

2018

Proceedings of the Institution of Mechanical Engineers, Part G:

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In this paper, a parallel model adaptive Kalman filtering algorithm is presented for multiple sensors estimation fusion when the measurement noise statistics are uncertain. As a typical adaptive filtering algorithm, the multiple model adaptive estimation tries to reduce the dependency of the filter on the noise parameters. It utilizes multiple models with different noise levels to estimate the state and combines the model-dependent estimates with model probability. However, with the increase in the number of active sensors, a large number of models are required to cover the entire range of the possible noise parameter values, which can become computationally infeasible. The main goal of this work is to incorporate the noise statistic estimator in the framework of the multiple model adaptive estimation, such that only two models are required for each sensor, which significantly reduce the complexity of the estimator. The advantage of the presented algorithm to deal with the model uncertainty is studied analytically. The high performance of the parallel model adaptive Kalman filtering for autonomous satellite navigation using inter-satellite line-of-sight measurements is illustrated in comparison with a robust Kalman filter, an intrinsically Bayesian robust Kalman filter, and the traditional multiple model adaptive estimation.

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“…In order to cope with this problem, it is important to improve the autonomous navigation capability for the spacecraft without the support of the radio beacons, especially in cases of emergencies. [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.…”

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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Calibration of Satellite Autonomous Navigation Based on Attitude Sensor

Cited by 8 publications

References 7 publications

Integrated celestial navigation for spacecraft using interferometer and Earth sensor

Integrated celestial navigation for spacecraft using interferometer and Earth sensor

Parallel model adaptive Kalman filtering for autonomous navigation with line-of-sight measurements

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

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