scholarly journals Autonomous Orbit Determination System of Navigation Satellite Based on Spaceborne GPS Technology

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Li Yang ◽  
Haote Ruan ◽  
Yunhan Zhang
Keyword(s):  
Orbit Determination ◽  
Unscented Kalman Filter ◽  
Satellite Orbit ◽  
Filter Method ◽  
Variance Matrix ◽  
Champ Satellite ◽  
Autonomous Orbit Determination ◽  
Determination System ◽  
Coarse Error ◽  
Kalman Filter Method

In recent years, many low-orbit satellites have been widely used in the field of scientific research and national defense in China. In order to meet the demand of high-precision satellite orbit in China’s space, surveying and mapping, and other related fields, navigation satellites are of great significance. The UKF (unscented Kalman filter) method is applied to space targets’ spaceborne GPS autonomous orbit determination. In this paper, the UKF algorithm based on UT transformation is mainly introduced. In view of the situation that the system noise variance matrix is unknown or the dynamic model is not accurate, an adaptive UKF filtering algorithm is proposed. Simulation experiments are carried out with CHAMP satellite GPS data, and the results show that the filtering accuracy and stability are improved, which proves the algorithm’s effectiveness. The experimental results show that the Helmert variance component estimation considering the dynamics model can solve the problem of reasonable weight determination of BDS/GPS observations and effectively weaken the influence of coarse error and improve the accuracy of orbit determination. The accuracy of autonomous orbit determination by spaceborne BDS/GPS is 1.19 m and 2.35 mm/s, respectively.

scholarly journals Analysis of Filtering Methods for Satellite Autonomous Orbit Determination Using Celestial and Geomagnetic Measurement

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Xiaolin Ning ◽  
Xin Ma ◽  
Cong Peng ◽  
Wei Quan ◽  
Jiancheng Fang
Keyword(s):  
Kalman Filter ◽  
Orbit Determination ◽  
Unscented Kalman Filter ◽  
Satellite Orbit ◽  
Filtering Method ◽  
Unscented Particle Filter ◽  
Autonomous Orbit Determination ◽  
Determination System ◽  
Orbit Dynamic ◽  
Satellite Orbit Dynamic

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.

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.

scholarly journals Estimation of Full Strap-down Rotating Bomb Guidance Information Based on Unscented Kalman Filter

2020 ◽  
Vol 38 (3) ◽  
pp. 604-609
Author(s):  
Wei Gao ◽  
Benbing Gao ◽  
Hongsong Fang ◽  
Xin Lu
Keyword(s):  
Mathematical Model ◽  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Angular Rate ◽  
The Body ◽  
Filter Method ◽  
Measurement Information ◽  
Kalman Filter Method ◽  
The Mathematical Model ◽  
Navigation Information

In this paper, the full strap-down seeker of rotating bomb is taken as the research object, and the method of extracting the LOS (line-of-sight) angle and angular rate of the full strap-down seeker of the rotating bomb is studied. The structure of the full strap-down seeker is quite different from that of the conventional rate gyro seeker. The measurement system of full strap-down seeker is fixed to the missile, the seeker can only obtain the measurement information in the projectile coordinate system, and the measurement information is coupled with the body posture information, so it cannot be directly used for the control guidance of the rotating projectile. First, based on the conversion relationship between coordinate systems, the mathematical model of the inertial LOS angle of the rotating bomb is established, and the mathematical model of the extraction of the inertial LOS angle and angular rate of the rotating bomb is further established. Then, the Kalman filter is designed by using the unscented Kalman filter method (UKF), and the extracted LOS angle containing noise information is filtered. Finally, the mathematical simulation is carried out to verify the validity of the mathematical model of LOS angle and angular rate extraction. Compared with the Extended Kalman filter method (EKF), the UKF has a higher accuracy for estimating the navigation information of the full strap-down rotating projectile.

scholarly journals General Model and SOC Estimation of Battery

2021 ◽  
Vol 15 ◽  
pp. 155-163
Author(s):  
Li Meng ◽  
Haipeng Guo ◽  
Xiaowei Zhao
Keyword(s):  
Kalman Filter ◽  
General Model ◽  
Unscented Kalman Filter ◽  
Measurement Data ◽  
Work Conditions ◽  
Filter Method ◽  
Soc Estimation ◽  
The Difference ◽  
Kalman Filter Method ◽  
Set Up

Monitoring the battery state is of great importance for the safety and normal of the systems which are powered by batteries. SOC (State of Charge) is one of the most important state parameters of battery. SOC cannot be measured directly. The Kalman filter algorithm is one of the techniques often applied to estimate SOC value. An accurate model is necessary for this algorithm. In this paper, a general SOC model is set up. It takes into account not only the difference between discharging and charging work conditions, but also the influence of the working atmosphere, such as temperature and discharging rate. Then based on this general model, unscented Kalman filter method is used to predict the SOC value. It can avoid the error which is caused by ignoring high-order terms, which is a shortcoming exist in the extended Kalman filter method. The simulation experiments prove the approach can get satisfactory results even when the measurement data is mixed with noise or the initial SOC value is not accurate.

scholarly journals A Discussion Related to Orbit Determination Using Nonlinear Sigma Point Kalman Filter

2009 ◽  
Vol 2009 ◽  
pp. 1-12 ◽  
Author(s):  
Paula Cristiane Pinto Mesquita Pardal ◽  
Helio Koiti Kuga ◽  
Rodolpho Vilhena de Moraes
Keyword(s):  
Kalman Filter ◽  
Dynamic Model ◽  
Extended Kalman Filter ◽  
Orbit Determination ◽  
Unscented Kalman Filter ◽  
Satellite Orbit ◽  
Observation Model ◽  
Gps Measurements ◽  
Unscented Transformation ◽  
Sigma Point

Herein, the purpose is to present a Kalman filter based on the sigma point unscented transformation development, aiming at real-time satellite orbit determination using GPS measurements. First, a brief review of the extended Kalman filter will be done. After, the sigma point Kalman filter will be introduced as well as the basic idea of the unscented transformation, in which this filter is based. Following, the unscented Kalman filter applied to orbit determination will be explained. Such explanation encloses formulations about the orbit determination through GPS; the dynamic model; the observation model; the unmodeled acceleration estimation; also an application of this new filter approaches on orbit determination using GPS measurements discussion.

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