Orbit Determination Using Pulsar Timing Data and Orientation Vector

Zhang, Hua; Rong, Jian; Xu, Luping

doi:10.1017/s0373463318000632

Cited by 7 publications

(8 citation statements)

References 31 publications

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“…On the principles of UKF, a augmented state unscented Kalman filter (ASUKF) is proposed by regarding the system bias as a slowly time-varying augmented state; the simulation confirms its effectiveness and reliability [9]. Furthermore, a novel approach of the modified strong tracking Kalman filter (STUKF) is designed, which had introduced a defined suboptimal fading factor based on the orthogonality principle of the innovation vectors in the framework of the UKF into the prediction covariance to adjust the Kalman gain matrix; the effectiveness of the STUKF is demonstrated in the simulation experiments [10]. Recently, a lot of novel algorithms have been developed quickly, an Adaptive Divided Difference Filter (ADDF) algorithm is introduced, which had optimized pulsar's position vector error by maximizing the incident flux density recorded by the X-ray detector; its simulation results had revealed that the navigation performance of ADDF can be improved by 70% compared with that of UKF and DDF [11].…”

Section: Introductionmentioning

confidence: 79%

“…where α is the right ascension angle, ∆α i is the first-order differential of right ascension, ∆α i 2 is the second-order differential of right ascension, δ is the real declination angle, ∆δ i is the first-order differential of the real declination angle, and ∆δ i 2 is the second-order of differential of the real declination angle. Then, combining Equation (10), the state equation of MASEKF is subsequently obtained. It can be shown as follows.…”

Section: The State Equationmentioning

confidence: 99%

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An Improved Augmented Algorithm for Direction Error in XPNAV

Ren

Nie

2020

Symmetry

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Recently, X-ray pulsar-based navigation (XPNAV) as a significant navigation method has been widely used in deep space exploration. However, the accuracy of XPNAV is limited to the existence of the pulsar direction error. To improve the performance of XPNAV, we have proposed a novel algorithm named “the modified augmented state extended Kalman filter” (MASEKF). The algorithm considers the high-order terms of direction error and then adds a more precise direction error into state equation and measurement equation. In the simulation, by comparing the performance of MASEKF, EKF, and ASEKF at the same time, it is found that MASEKF has better performance in the accuracy and stability, and the results also demonstrate that MASEKF algorithm has faster convergence speed. This paper provides a strong reference for other improvements of algorithms towards direction error. The purpose of this study is to establish MASEKF and add the direction error into the measurement equation and the state equation, so as to realize the coordination and symmetry of the algorithm.

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Section: Introductionmentioning

confidence: 79%

Section: The State Equationmentioning

confidence: 99%

An Improved Augmented Algorithm for Direction Error in XPNAV

Ren

Nie

2020

Symmetry

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“…The StarNAV is an attractive autonomous navigation approach for spacecrafts as the observed targets are stars, which distribute widely on the celestial sphere, and it is easier to capture at different locations and observe with high accuracy than the near celestial body (such as Earth, Moon and planets) used for the traditional celestial navigation system (Zhao et al , 2017). Generally, the optical signal from stars is stronger than the signal from pulsars used for the X-ray pulsar-based navigation system (Wang et al , 2015; Zhang et al , 2019; Gui et al , 2019). The positioning accuracy of the StarNAV method would degrade as the distance between the spacecraft and the main celestial body increases.…”

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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“…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] . Among these existing methods, the bearings-only autonomous navigation has the potential to achieve high accuracy with current technology [15][16][17] .…”

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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Orbit Determination Using Pulsar Timing Data and Orientation Vector

Cited by 7 publications

References 31 publications

An Improved Augmented Algorithm for Direction Error in XPNAV

An Improved Augmented Algorithm for Direction Error in XPNAV

Integrated autonomous optical navigation using Q-Learning extended Kalman filter

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

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