A Novel Autonomous Celestial Navigation Method Using Solar Oscillation Time Delay Measurement

Ning, Xiaolin; Gui, Mingzhen; Fang, Jiancheng; Liu, Gang; Wu, Wenqi

doi:10.1109/taes.2018.2791038

Cited by 22 publications

(14 citation statements)

References 30 publications

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“…The mathematical induction is employed to prove the conclusion (12). First, it is easy to see from (3) and (11) that Q 1 (s, a) ≥ Q 0 (s, a) ( C 1 ) which means that the conclusion (12) holds for i = 0. Next, assume that the conclusion (12) holds for index l − 1, i.e., The mathematical induction is employed to prove that…”

Section: Proof Of Lemma 43mentioning

confidence: 99%

“…The spacecraft autonomous navigation system aims to determine the orbit of the spacecraft using onboard measurement information without the support of ground-based tracking. It plays a critical role for the success of space missions, especially in the case that the failure occurs in the communication system toward the ground stations [1][2][3][4][5] . Many autonomous navigation schemes have been proposed, such as satellite stellar refraction navigation [6,7] , autonomous navigation using visible planets [8][9][10] and X-ray pulsar-based navigation (XNAV) [11][12][13][14] .…”

Section: Introductionmentioning

confidence: 99%

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Q-Learning-Based Target Selection for Bearings-Only Autonomous Navigation

Xiong¹,

Wei²

2021

J Syst Sci Complex

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This paper presents a Q-learning-based target selection algorithm for spacecraft autonomous navigation using bearing observations of known visible targets. For the considered navigation system, the position and velocity of the spacecraft are estimated using an extended Kalman filter (EKF) with the measurements of inter-satellite line-of-sight (LOS) vectors obtained via an onboard star camera. This paper focuses on the selection of the appropriate target at each observation period for the star camera adaptively, such that the performance of the EKF is enhanced. To derive an effective algorithm, a Q-function is designed to select a proper observation region, while a U-function is introduced to rank the targets in the selected region. Both the Q-function and the U-function are constructed based on the sequence of innovations obtained from the EKF. The efficiency of the Q-learning-based target selection algorithm is illustrated via numerical simulations, which show that the presented algorithm outperforms the traditional target selection strategy based on a Cramer-Rao bound (CRB) in the case that the prior knowledge about the target location is inaccurate.

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Section: Proof Of Lemma 43mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Q-Learning-Based Target Selection for Bearings-Only Autonomous Navigation

Xiong¹,

Wei²

2021

J Syst Sci Complex

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“…Second, using Sun instead of stars, the solar spectrum measured directly by the spacecraft could be compared with the spectrum reflected off a nearby celestial body with a well-known ephemeris. Although this has been proposed in the past [65,66], additional work is required to determine the true efficacy of such an approach.…”

Section: Preliminary Feasibility Assessment Of Starnav Measurementsmentioning

confidence: 99%

“…Of note is that many of these complications may be avoided within the context of relative navigation by comparing the spectra observed at more than one location, thus, allowing one to estimate the relative velocity between the two observers. Applications of this approach include formation flying [64] or orbit determination by reflected sunlight [65,66,67]. However, the comparison of simultaneously recorded spectra at two different locations is not compatible with the philosophy of autonomous navigation and is not explored further in this work.…”

Section: Introductionmentioning

confidence: 99%

StarNAV: Autonomous Optical Navigation of a Spacecraft by the Relativistic Perturbation of Starlight

Christian

2019

Sensors

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Future space exploration missions require increased autonomy. This is especially true for navigation, where continued reliance on Earth-based resources is often a limiting factor in mission design and selection. In response to the need for autonomous navigation, this work introduces the StarNAV framework that may allow a spacecraft to autonomously navigate anywhere in the Solar System (or beyond) using only passive observations of naturally occurring starlight. Relativistic perturbations in the wavelength and direction of observed stars may be used to infer spacecraft velocity which, in turn, may be used for navigation. This work develops the mathematics governing such an approach and explores its efficacy for autonomous navigation. Measurement of stellar spectral shift due to the relativistic Doppler effect is found to be ineffective in practice. Instead, measurement of the change in inter-star angle due to stellar aberration appears to be the most promising technique for navigation by the relativistic perturbation of starlight.

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“…The difference between them can reflect the radial distance information. Ning also carried out related research in this field, and proposed a solar oscillation time-delay measurement-assisted celestial navigation method (Ning et al, 2017; Ning et al, 2018a). In addition, these methods can also be used for relative navigation (Liu et al, 2017a; Wang et al, 2020; Yu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Mars's Moons-Induced Time Dispersion Analysis for Solar TDOA Navigation

Liu

Ning³

et al. 2020

J. Navigation

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The time dispersion effect affects the accuracy of solar time difference of arrival (TDOA) navigation. In this celestial autonomous navigation, Mars's moons are reflecting celestial bodies, and their shape affects the TDOA dispersion model. In the modelling process of traditional methods, the moons of Mars (Phobos and Deimos) are regarded as points, which causes the model to be inaccurate. In order to solve these problems, we simplified the Mars's moons into ellipsoids or solid diamonds, and then established a TDOA model with the nonspherical Mars's moons as reflecting celestial bodies through differential geometry and geometric optics. Finally, we analysed the time dispersion caused by the Mars's moons in theory. Theoretical analysis and experiments show that the point model error is 5·66 km, and the 3D model error is within 70 m. Thus, the 3D TDOA model established in this paper is meaningful. In addition, the Sun–Mars-moons–spacecraft angle, solar flare, three-axis length, and attitude of the Mars's moons have a great effect on the dispersion profile, while the Mars's moons-to-spacecraft distance has a small effect.

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A Novel Autonomous Celestial Navigation Method Using Solar Oscillation Time Delay Measurement

Cited by 22 publications

References 30 publications

Q-Learning-Based Target Selection for Bearings-Only Autonomous Navigation

Q-Learning-Based Target Selection for Bearings-Only Autonomous Navigation

StarNAV: Autonomous Optical Navigation of a Spacecraft by the Relativistic Perturbation of Starlight

Mars's Moons-Induced Time Dispersion Analysis for Solar TDOA Navigation

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