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ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2023, Vol. 44 ›› Issue (3): 526232.doi: 10.7527/S1000-6893.2021.26232

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Pulsar/inter-satellite LOS integrated navigation based on augmented QLEKF

Kai XIONG(), Chunling WEI, Liansheng LI, Peng ZHOU   

  1. Science and Technology on Space Intelligent Control Laboratory,Beijing Institute of Control Engineering,Beijing 100094,China
  • Received:2021-08-16 Revised:2021-08-23 Accepted:2021-09-25 Online:2023-02-15 Published:2021-10-12
  • Contact: Kai XIONG E-mail:tobelove2001@vip.tom.com
  • Supported by:
    Civil Aerospace Advance Research Project(D020403)

Abstract:

An integrated navigation method based on X-ray pulsars and inter-satellite measurements is designed for constellation satellite autonomous navigation with high-accuracy requirement. The X-ray detector is used to obtain the pulse arrival time. The relative position vector measurement between satellites is obtained from star cameras and inter-satellite links. A navigation filter is designed to process the obtained data, so as to estimate the kinematic states of the satellites that participate in the navigation. To cope with the problem that the ephemeris error of Earth affects the integrated navigation performance, the position vector of Earth relative to the solar system barycenter is augmented as a state vector, and an augmented extended Kalman filter is designed. The position vector of Earth for navigation is estimated in real time, such that the effect of the ephemeris error of Earth is suppressed. In addition, to cope with the problem that state estimation accuracy is affected by the choice of filtering parameters, a Q-Learning Extended Kalman Filter (QLEKF) is designed. The key idea is to select the appropriate filtering parameters adaptively with the Q-learning method, which has the decision-making ability. It is illustrated via numerical simulations that effect of the ephemeris error of Earth on the autonomous navigation performance of constellation is decreased efficiently using the proposed algorithm. The QLEKF is superior to traditional filtering algorithms in positioning accuracy.

Key words: X-ray pulsar navigation, ephemeris error, extended Kalman filter, Q-learning, satellite constellation

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