Comparative study on autonomous navigation for Mars cruise probe based on observability analysis

Gu, Long; Li, Shuang; Li, Wendan; Sun, Jun

doi:10.1117/1.jatis.4.4.048001

Cited by 3 publications

(5 citation statements)

References 27 publications

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“…This section explains how the Q-learning method is designed for target selection with the system model shown in (1) and (2). As one of the most widely used reinforcement learning (RL) approaches, it involves an agent operating in an uncertain environment and attempting to select the best action via a trial-and-error searching process.…”

Section: Q-learning-based Target Selectionmentioning

confidence: 99%

“…The model structure for the navigation system is described in (1) and (2). The state vector x k consists of the position and velocity vectors of the spacecraft…”

Section: Autonomous Navigation Systemmentioning

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: Q-learning-based Target Selectionmentioning

confidence: 99%

“…The model structure for the navigation system is described in (1) and (2). The state vector x k consists of the position and velocity vectors of the spacecraft…”

Section: Autonomous Navigation Systemmentioning

confidence: 99%

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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“…Ma等人 [12,13] 提出了基于光学相机、脉冲星测量和星间无线电测量的组合导航方法, 此方法采用集中式融合滤波策略提高了探测器巡航段的自主导航精度. Gu等人 [14,15] 进一步采用可观测性分析的方法对比研究了在火星探测巡航段末端不同组合导航方案的导航效果. Wang等人 [16] 针对火星探测器接近段采用基于脉冲星和火星环绕探测器相对测量的方法, 实现了对探测器的自主导航.…”

Section: 另外基于探测器间无线电测量和惯性导航的方法是unclassified

Integrated navigation for the approach phase of Mars probe

Li¹,

Yan²,

Jiang³

et al. 2020

Sci. Sin.-Tech.

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“…As the number of spacecrafts with various missions continues to grow, designers attempt to develop spacecrafts that can operate at increasing level of autonomy. 1–5 An autonomous navigation system offers position, velocity, and attitude information based on onboard instruments, rather than ground stations. It is valuable to reduce the dependence of spacecrafts on ground-based operation.…”

Section: Introductionmentioning

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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Comparative study on autonomous navigation for Mars cruise probe based on observability analysis

Cited by 3 publications

References 27 publications

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

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

Integrated navigation for the approach phase of Mars probe

Integrated celestial navigation for spacecraft using interferometer and Earth sensor

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