基于扩维QLEKF的脉冲星/星间定向组合导航

doi:10.7527/S1000-6893.2021.26232

Abstract

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

CLC Number:

V324.2⁺4

Kai XIONG, Chunling WEI, Liansheng LI, Peng ZHOU. Pulsar/inter-satellite LOS integrated navigation based on augmented QLEKF[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2023, 44(3): 526232.

Figures/Tables 13

Fig.1

Fig. 2

Fig. 3

Fig. 4

Fig.5

Fig. 6

Table 1

Table 2

Simulation configuration for constellation autonomous navigation

类别	参数	设置
仿真设置	仿真时间/d	4
	预测时间步长/s	10
	卫星初始位置误差	$N (0, (100 m) 2)$
	卫星初始速度误差	$N (0, (0.01 m / s) 2)$
	地球初始位置误差	$N (0, (300 m) 2)$
	地球初始速度误差	$N (0, (0.03 m / s) 2)$
	脉冲星数据更新频率/Hz	0.001
	星间观测数据更新频率/Hz	0.1

滤波器参数	系统噪声方差阵	$Q ¯ k = σ r 2 I 3 × 3 σ v 2 I 3 × 3 ⋱$ $σ r = 5 × 10 - 4 m$ $σ v = 5 × 10 - 4 m / s$
	测量噪声方差阵	$R X, k (p) = (σ X (p)) 2$ $R L, k (m n) = σ L (m n) 2 I 3 × 3$
	初始估计误差方差阵	$P 0 = p r 2 I 3 × 3 p v 2 I 3 × 3 ⋱$ $p r = 300 m$ $p v = 0.03 m / s$

Q学习参数	学习速率 $α$	$0.1$
	折扣因子 $γ$	$0.9$
	随机动作选择概率 $ε$	$0.1$
	模型集中元素个数 $M$	$9$

Table 2

Fig.7

Fig.8

Fig. 9

Fig.10

Table 3

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卫星编号	1	2	3
半长轴/km	27 904	27 905	27 906
偏心率/10^-5	2.0	2.0	2.0
轨道倾角/（°）	55	55	55
升交点赤经/（°）	120	240	0
近地点幅角/（°）	0	0	0
平近点角/（°）	135	165	195

滤波算法	位置估计均方根误差/m
滤波算法	卫星 1	卫星 2	卫星 3
扩维前EKF	673.1	674.4	677.3
REKF	60.6	60.5	60.8
扩维EKF	41.6	41.9	41.5
AEKF	39.4	40.3	39.6
MMAE	28.1	28.1	27.6
QLEKF	22.4	22.4	22.2

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

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