An Interplanetary Network for Spacecraft Autonomous Navigation

Xin, Shijun; Wang, Yidi; Zheng, Wei; Ye, Meng; Zhang, David

doi:10.1017/s0373463318000309

Cited by 10 publications

(4 citation statements)

References 19 publications

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“…The effectiveness of the presented algorithm is validated through the simulation of spacecraft autonomous navigation, which is an important technique to improve the survivability of spacecrafts [36][37][38]. Suppose that there are three satellites with star cameras and inter-satellite links, which provide the relative position vector measurements among the satellites.…”

Section: Illustrative Examplementioning

confidence: 99%

Q‐learning for noise covariance adaptation in extended KALMAN filter

Xiong¹,

Wei²,

Zhang³

2020

Asian Journal of Control

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The extended Kalman filter (EKF) is a widely used method in navigation applications. The EKF suffers from noise covariance uncertainty, potentially causing it to perform poorly in practice. This paper attempts to suppress the unfavorable effect of the noise covariance uncertainty to the EKF in the framework of reinforcement learning. The proposed state estimation algorithm combines the EKF and a Q-learning method, where a covariance adaptation strategy is designed based on the Q-values, leading to a gradual improvement in the estimation performance. The resultant algorithm is called the Q-learning extended Kalman filter (QLEKF), which is less sensitive to the noise covariance uncertainty. To evaluate the estimation error behavior in nonlinear uncertain systems, the stability of the filtering algorithm is investigated. A numerical simulation for spacecraft autonomous navigation is implemented to demonstrate the efficiency of the QLEKF. It is shown that the presented algorithm outperforms a traditional EKF and an adaptive extended Kalman filter (AEKF) in estimation accuracy.

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Section: Illustrative Examplementioning

confidence: 99%

Q‐learning for noise covariance adaptation in extended KALMAN filter

Xiong¹,

Wei²,

Zhang³

2020

Asian Journal of Control

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“…Recently, autonomous navigation technology, which can provide the position and velocity information of spacecrafts using onboard instruments, has attracted increasing attentions (Ning et al , 2018; Xin et al , 2018; Li et al , 2018). The StarNAV (Star Navigation) presented by Calabro (2011) and Christian (2019) is an autonomous navigation method based on the measurement of the inter-star angle, which is the angle subtended at the spacecraft between the line-of-sight (LOS) vectors to two stars and related to the relativistic perturbations.…”

Section: Introductionmentioning

confidence: 99%

Integrated autonomous optical navigation using Q-Learning extended Kalman filter

Xiong¹,

Wei²,

Zhou³

2022

AEAT

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Purpose This paper aims to improve the performance of the autonomous optical navigation using relativistic perturbation of starlight, which is a promising technique for future space missions. Through measuring the change in inter-star angle due to the stellar aberration and the gravitational deflection of light with space-based optical instruments, the position and velocity vectors of the spacecraft can be estimated iteratively. Design/methodology/approach To enhance the navigation performance, an integrated optical navigation (ION) method based on the fusion of both the inter-star angle and the inter-satellite line-of-sight measurements is presented. A Q-learning extended Kalman filter (QLEKF) is designed to optimize the state estimate. Findings Simulations illustrate that the integrated optical navigation outperforms the existing method using only inter-star angle measurement. Moreover, the QLEKF is superior to the traditional extended Kalman filter in navigation accuracy. Originality/value A novel ION method is presented, and an effective QLEKF algorithm is designed for information fusion.

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“…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%

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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An Interplanetary Network for Spacecraft Autonomous Navigation

Cited by 10 publications

References 19 publications

Q‐learning for noise covariance adaptation in extended KALMAN filter

Q‐learning for noise covariance adaptation in extended KALMAN filter

Integrated autonomous optical navigation using Q-Learning extended Kalman filter

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

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