Observability Analysis and Navigation Algorithm for Distributed Satellites System Using Relative Range Measurements

Su, Qiya; Huang, Yi

doi:10.1007/s11424-018-6096-1

Cited by 4 publications

(3 citation statements)

References 20 publications

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“…In the following lemma, we show that Θ i (s, a) remains bounded under certain conditions. Lemma 4.1 Consider the sequence {Θ i (s, a)} generated by (4). If there exist constants δ ≥ 1 ≥ δ ≥ 0 and γ ≥ 0, such that the following inequalities hold…”

Section: Iteration Process Analysismentioning

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%

“…Consider the sequence {Θ i (s, a)} generated by(4). Suppose that the conclusion of Lemma 4.1 shown in (8) is satisfied.…”

mentioning

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: Iteration Process Analysismentioning

confidence: 99%