Conservative Term Constrained Kalman Filter for Autonomous Orbit Determination

Satellite autonomous orbit determination (OD) is a complex process using filtering method to integrate observation and orbit dynamic equations effectively and estimate the position and velocity of a satellite. Therefore, the filtering method plays an important role in autonomous orbit determination accuracy and time consumption. Extended Kalman filter (EKF), unscented Kalman filter (UKF), and unscented particle filter (UPF) are three widely used filtering methods in satellite autonomous OD, owing to the nonlinearity of satellite orbit dynamic model. The performance of the system based on these three methods is analyzed under different conditions. Simulations show that, under the same condition, the UPF provides the highest OD accuracy but requires the highest computation burden. Conclusions drawn by this study are useful in the design and analysis of autonomous orbit determination system of satellites.

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Magnetometer-based autonomous orbit determination via a measurement differencing extended Kalman filter during geomagnetic storms

Aircraft Engineering and Aerospace Technology ◽

10.1108/aeat-03-2019-0053 ◽

2020 ◽

Vol 92 (3) ◽

pp. 428-439

Author(s):

Feng Cui ◽

Dong Gao ◽

Jianhua Zheng

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

Orbit Determination ◽

Geomagnetic Field ◽

Field Model ◽

Geomagnetic Storms ◽

Content Type ◽

Geomagnetic Field Model ◽

Autonomous Orbit Determination ◽

Time Accuracy

Purpose The main reason for the low accuracy of magnetometer-based autonomous orbit determination is the coarse accuracy of the geomagnetic field model. Furthermore, the geomagnetic field model error increases obviously during geomagnetic storms, which can still further reduce the navigation accuracy. The purpose of this paper is to improve the accuracy of magnetometer-based autonomous orbit determination during geomagnetic storms. Design/methodology/approach In this paper, magnetometer-based autonomous orbit determination via a measurement differencing extended Kalman filter (MDEKF) is studied. The MDEKF algorithm can effectively remove the time-correlated portion of the measurement error and thus can evidently improve the accuracy of magnetometer-based autonomous orbit determination during geomagnetic storms. Real flight data from Swarm A are used to evaluate the performance of the MDEKF algorithm presented in this study. A performance comparison between the MDEKF algorithm and an extended Kalman filter (EKF) algorithm is investigated for different geomagnetic storms and sampling intervals. Findings The simulation results show that the MDEKF algorithm is superior to the EKF algorithm in terms of estimation accuracy and stability with a short sampling interval during geomagnetic storms. In addition, as the size of the geomagnetic storm increases, the advantages of the MDEKF algorithm over the EKF algorithm become more obvious. Originality/value The algorithm in this paper can improve the real-time accuracy of magnetometer-based autonomous orbit determination during geomagnetic storms with a low computational burden and is very suitable for low-orbit micro- and nano-satellites.

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Autonomous Orbit Determination for a Hybrid Constellation

International Journal of Aerospace Engineering ◽

10.1155/2018/4843061 ◽

2018 ◽

Vol 2018 ◽

pp. 1-13

Author(s):

Muzi Li ◽

Bo Xu ◽

Jun Sun

Keyword(s):

Remote Sensing ◽

Monte Carlo ◽

Kalman Filter ◽

Orbit Determination ◽

Main Idea ◽

Altimeter Data ◽

Range Data ◽

Leo Satellites ◽

Autonomous Orbit Determination ◽

Hybrid Constellation

A new orbit determination scheme targeting communication and remote sensing satellites in a hybrid constellation is investigated in this paper. We first design one such hybrid constellation with a two-layer configuration (LEO/MEO) by optimizing coverage and revisit cycle. The main idea of the scheme is to use a combination of imagery, altimeter data, and inter-satellite range data as measurements and determine orbits of the satellites in the hybrid constellation with the help of the extended Kalman filter (EKF). The performance of the new scheme is analyzed with Monte Carlo simulations. We first focus on an individual remote sensing satellite and compared the performance of orbit determination using only imagery with its counterpart using both imagery and altimeter measurements. Results show that the performance improves when imagery is used with altimeter data pointing to geometer calibration sites but declines when used with ocean altimeter data. We then expand the investigation to the whole constellation. When inter-satellite range data is added, orbits of all the satellites in the hybrid constellation can be autonomously determined. We find that the combination of inter-satellite range data with remote sensing observations lead to a further improvement in orbit determination precision for LEO satellites. Our results also show that the performance of the scheme would be affected when remote sensing observations on certain satellites are absent.

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Geomagnetic orbit determination: EKF or UKF?

Aircraft Engineering and Aerospace Technology ◽

10.1108/aeat-11-2020-0279 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Jin Wu ◽

Ming Liu ◽

Chengxi Zhang ◽

Yulong Huang ◽

Zebo Zhou

Keyword(s):

Kalman Filter ◽

Orbit Determination ◽

Unscented Kalman Filter ◽

Nonlinear Models ◽

Computational Time ◽

Content Type ◽

Spacecraft Navigation ◽

Highly Nonlinear ◽

Autonomous Orbit Determination ◽

Spherical Harmonic Models

Purpose Autonomous orbit determination using geomagnetic measurements is an important backup technique for safe spacecraft navigation with a mere magnetometer. The geomagnetic model is used for the state estimation of orbit elements, but this model is highly nonlinear. Therefore, many efforts have been paid to developing nonlinear filters based on extended Kalman filter (EKF) and unscented Kalman filter (UKF). This paper aims to analyze whether to use UKF or EKF in solving the geomagnetic orbit determination problem and try to give a general conclusion. Design/methodology/approach This paper revisits the problem and from both the theoretical and engineering results, the authors show that the EKF and UKF show identical estimation performances in the presence of nonlinearity in the geomagnetic model. Findings While EKF consumes less computational time, the UKF is computationally inefficient but owns better accuracy for most nonlinear models. It is also noted that some other navigation techniques are also very similar with the geomagnetic orbit determination. Practical implications The intrinsic reason of such equivalence is because of the orthogonality of the spherical harmonics which has not been discovered in previous studies. Thus, the applicability of the presented findings are not limited only to the major problem in this paper but can be extended to all those schemes with spherical harmonic models. Originality/value The results of this paper provide a fact that there is no need to choose UKF as a preferred candidate in orbit determination. As UKF achieves almost the same accuracy as that of EKF, its loss in computational efficiency will be a significant obstacle in real-time implementation.

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Research on the Effectiveness of Different Estimation Algorithm on the Autonomous Orbit Determination of Lagrangian Navigation Constellation

International Journal of Aerospace Engineering ◽

10.1155/2016/8392148 ◽

2016 ◽

Vol 2016 ◽

pp. 1-8

Author(s):

Youtao Gao ◽

Junkang Chen ◽

Bo Xu ◽

Jianhua Zhou

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

Orbit Determination ◽

Estimation Method ◽

Estimation Algorithm ◽

Fading Memory ◽

Variance Matrix ◽

Autonomous Orbit Determination ◽

Navigation Constellation

The accuracy of autonomous orbit determination of Lagrangian navigation constellation will affect the navigation accuracy for the deep space probes. Because of the special dynamical characteristics of Lagrangian navigation satellite, the error caused by different estimation algorithm will cause totally different autonomous orbit determination accuracy. We apply the extended Kalman filter and the fading–memory filter to determinate the orbits of Lagrangian navigation satellites. The autonomous orbit determination errors are compared. The accuracy of autonomous orbit determination using fading-memory filter can improve 50% compared to the autonomous orbit determination accuracy using extended Kalman filter. We proposed an integrated Kalman fading filter to smooth the process of autonomous orbit determination and improve the accuracy of autonomous orbit determination. The square root extended Kalman filter is introduced to deal with the case of inaccurate initial error variance matrix. The simulations proved that the estimation method can affect the accuracy of autonomous orbit determination greatly.

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