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

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.

Geomagnetic orbit determination: EKF or UKF?

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.

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

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.

scholarly journals CM6: a comprehensive geomagnetic field model derived from both CHAMP and Swarm satellite observations

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
Terence J. Sabaka ◽  
Lars Tøffner-Clausen ◽  
Nils Olsen ◽  
Christopher C. Finlay
Keyword(s):  
Geomagnetic Field ◽  
Field Model ◽  
Satellite Observations ◽  
Geomagnetic Field Model ◽  
Swarm Satellite

Autonomous navigation algorithm based on AUKF filter about fusion of geomagnetic and sunlight directions

2018 ◽  
Vol 11 (4) ◽  
pp. 471-485 ◽  
Author(s):  
Bing Hua ◽  
Zhiwen Zhang ◽  
Yunhua Wu ◽  
Zhiming Chen
Keyword(s):  
Orbit Determination ◽  
Geomagnetic Field ◽  
Autonomous Navigation ◽  
Unscented Kalman Filter ◽  
Satellite Orbit ◽  
Field Vector ◽  
Low Earth Orbit ◽  
Fusion Algorithm ◽  
Content Type ◽  
Navigation Algorithm

Purpose The geomagnetic field vector is a function of the satellite’s position. The position and speed of the satellite can be determined by comparing the geomagnetic field vector measured by on board three-axis magnetometer with the standard value of the international geomagnetic field. The geomagnetic model has the disadvantages of uncertainty, low precision and long-term variability. Therefore, accuracy of autonomous navigation using the magnetometer is low. The purpose of this paper is to use the geomagnetic and sunlight information fusion algorithm to improve the orbit accuracy. Design/methodology/approach In this paper, an autonomous navigation method for low earth orbit satellite is studied by fusing geomagnetic and solar energy information. The algorithm selects the cosine value of the angle between the solar light vector and the geomagnetic vector, and the geomagnetic field intensity as observation. The Adaptive Unscented Kalman Filter (AUKF) filter is used to estimate the speed and position of the satellite, and the simulation research is carried out. This paper also made the same study using the UKF filter for comparison with the AUKF filter. Findings The algorithm of adding the sun direction vector information improves the positioning accuracy compared with the simple geomagnetic navigation, and the convergence and stability of the filter are better. The navigation error does not accumulate with time and has engineering application value. It also can be seen that AUKF filtering accuracy is better than UKF filtering accuracy. Research limitations/implications Geomagnetic navigation is greatly affected by the accuracy of magnetometer. This paper does not consider the spacecraft’s environmental interference with magnetic sensors. Practical implications Magnetometers and solar sensors are common sensors for micro-satellites. Near-Earth satellite orbit has abundant geomagnetic field resources. Therefore, the algorithm will have higher engineering significance in the practical application of low orbit micro-satellites orbit determination. Originality/value This paper introduces a satellite autonomous navigation algorithm. The AUKF geomagnetic filter algorithm using sunlight information can obviously improve the navigation accuracy and meet the basic requirements of low orbit small satellite orbit determination.

Sensorless finite-state predictive torque control of induction motor fed by four-switch inverter using extended Kalman filter

2018 ◽  
Vol 37 (6) ◽  
pp. 2006-2024 ◽  
Author(s):  
Mohamed Chebaani ◽  
Amar Goléa ◽  
Med Toufik Benchouia ◽  
Noureddine Goléa
Keyword(s):  
Kalman Filter ◽  
Induction Motor ◽  
Extended Kalman Filter ◽  
Direct Torque Control ◽  
Operating Conditions ◽  
Torque Control ◽  
Switching Frequency ◽  
Content Type ◽  
Finite State ◽  
Torque Ripples

Purpose Direct Torque Control (DTC) of induction motor drives is a well-established technique owing to features such as fast dynamic and insensibility to motor parameters. However, conventional DTC scheme, based on comparators and the switching table, suffers from large torque and flux ripples. To improve DTC performance, this study aims to propose and implement a sensorless finite-state predictive torque control using extended Kalman Filter in dSPACE environment. Design/methodology/approach This paper deals with the design of an extended Kalman filter for estimating the state of an induction motor model and for sensorless control of systems using this type of motor as an actuator. A complex-valued model is adopted that simultaneously allows a simpler observability analysis of the system and a more effective state estimation. Findings Simulation and experimental results reveal that the drive system, associated with this technique, can effectively reduce flux and torque ripples with better dynamic and steady state performance. Further, the proposed approach maintains a constant switching frequency. Originality/value The proposed speed observer have been developed and implemented experimentally under different operating conditions such as parameter variation, no-load/load disturbances and speed variations in different speed operation regions.

A time-efficient implementation of Extended Kalman Filter for sequential orbit determination and a case study for onboard application

2018 ◽  
Vol 62 (2) ◽  
pp. 343-358
Author(s):  
Jingshi Tang ◽  
Haihong Wang ◽  
Qiuli Chen ◽  
Zhonggui Chen ◽  
Jinjun Zheng ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Orbit Determination ◽  
Efficient Implementation

Geomagnetic Field Model Indicates Shrinking Northern Auroral Oval

2020 ◽  
Vol 125 (8) ◽  
Author(s):  
Bruno Zossi ◽  
Mariano Fagre ◽  
Hagay Amit ◽  
Ana G. Elias
Keyword(s):  
Geomagnetic Field ◽  
Field Model ◽  
Auroral Oval ◽  
Geomagnetic Field Model

Positioning of the Ionospheric Field-Aligned Irregularities With Geomagnetic Field Model

2020 ◽  
Vol 17 (8) ◽  
pp. 1338-1342
Author(s):  
Gang Chen ◽  
Shan Yi ◽  
Zhihua Wang ◽  
Chunxiao Yan ◽  
Ting Yong ◽  
...  
Keyword(s):  
Geomagnetic Field ◽  
Field Model ◽  
Geomagnetic Field Model ◽  
Field Aligned Irregularities

scholarly journals Distributed Orbit Determination for Global Navigation Satellite System with Inter-Satellite Link

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1031 ◽  
Author(s):  
Yuanlan Wen ◽  
Jun Zhu ◽  
Youxing Gong ◽  
Qian Wang ◽  
Xiufeng He
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Global Navigation Satellite System ◽  
Orbit Determination ◽  
Measurement Data ◽  
Satellite System ◽  
Navigation Satellite ◽  
Satellite Link ◽  
Global Navigation ◽  
Global Navigation Satellite

To keep the global navigation satellite system functional during extreme conditions, it is a trend to employ autonomous navigation technology with inter-satellite link. As in the newly built BeiDou system (BDS-3) equipped with Ka-band inter-satellite links, every individual satellite has the ability of communicating and measuring distances among each other. The system also has less dependence on the ground stations and improved navigation performance. Because of the huge amount of measurement data, the centralized data processing algorithm for orbit determination is suggested to be replaced by a distributed one in which each satellite in the constellation is required to finish a partial computation task. In the present paper, the balanced extended Kalman filter algorithm for distributed orbit determination is proposed and compared with the whole-constellation centralized extended Kalman filter, the iterative cascade extended Kalman filter, and the increasing measurement covariance extended Kalman filter. The proposed method demands a lower computation power; however, it yields results with a relatively good accuracy.

Sign in / Sign up

Export Citation Format

Share Document