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.

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.

Improving extended Kalman filter algorithm in satellite autonomous navigation

2016 ◽  
Vol 231 (4) ◽  
pp. 743-759 ◽  
Author(s):  
Lina He ◽  
Hairui Zhou ◽  
Gongyuan Zhang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Orbit Determination ◽  
Autonomous Navigation ◽  
Precise Orbit Determination ◽  
Satellite Orbit ◽  
Initial Position ◽  
Mean Value Theorem ◽  
Position Errors ◽  
Kalman Filter Algorithm

With the goal of reducing dependence on ground tracking systems, satellite autonomous navigation technologies are developed quickly in the recent several decades. However, precise orbit determination at high orbital altitudes is an important and challenging problem. In this paper, the nonlinear real-time orbit determination problem is investigated. Combined with satellite dynamical model, extended Kalman filter is explored to estimate satellite orbit parameters. Further, considering errors occur in linearization processing, two improvements for the extended Kalman filter algorithm, i.e. extended Kalman filter-I and extended Kalman filter-II, are proposed based on Lagrange’s mean value theorem, and respectively focus on choosing better linear expansion point and Jacobian matrix calculation point. Extensive simulations show that extended Kalman filter-I and extended Kalman filter-II significantly enhance orbit accuracy, compared with extended Kalman filter. And the increases in calculation complexity are acceptable. Finally, the robustness of extended Kalman filter-I and extended Kalman filter-II is analyzed by given different initial position errors, and results show that extended Kalman filter-I and extended Kalman filter-II have better robustness than extended Kalman filter.

GPS/INS Integrated Navigation Based on Unscented Kalman Filter

2013 ◽  
Vol 380-384 ◽  
pp. 3429-3433
Author(s):  
Wan Li Xu ◽  
Zhun Liu ◽  
Jun Hui Liu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Unscented Kalman Filter ◽  
Error Reduction ◽  
Effective Algorithm ◽  
Integrated Navigation ◽  
Unscented Transformation ◽  
Recursive Bayesian Estimation ◽  
Navigation Method ◽  
Optimal Implementation

[Purpose] In GPS/INS integrated navigation, which is widely used in high precision of the real-time navigation, the Extended Kalman Filter (EKF) has become one of the most widely used algorithms. Unfortunately, the EKF is based on a sub-optimal implementation of the recursive Bayesian estimation framework applied to Gaussian random variables. This can seriously affect the accuracy or even lead to divergence of the system. In order to improve the accuracy, we apply the Unscented Transformation to GPS/INS integrated navigation. [Method] This paper optimizes GPS/INS integrated navigation by applying the Unscented Kalman Filter (UKF) algorithm which is based on the Unscented Transformation. [Results] The experimental results show that the UKF has an error reduction of over 10% in every estimator relative to the EKF. [Conclusions] Consequently, the UKF is an effective algorithm to improve the accuracy of GPS/INS integrated navigation.

Real-Time Signal Denoising Algorithm in Wheel Force Transducer

2014 ◽  
Vol 615 ◽  
pp. 244-247
Author(s):  
Dong Wang ◽  
Guo Yu Lin ◽  
Wei Gong Zhang
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Extended Kalman Filter ◽  
Unscented Kalman Filter ◽  
Force Transducer ◽  
Time Signal ◽  
Signal Denoising ◽  
Observation Function ◽  
Simulation Results ◽  
Wheel Force Transducer

The wheel force transducer (WFT) is used to measure dynamic wheel loads. Unlike other force sensors, WFT is rotating with the wheel. For this reason, the outputs and the inputs of the transducer are nonlinearly related, and traditional Kalman Filter is not suitable. In this paper, a new real-time filter algorithm utilizing Quadrature Kalman Filter (QKF) is proposed to solve this problem. In Quadrature Kalman Filter, Singer model is introduced to track the wheel force, and the observation function is established for WFT. The simulation results illustrate that the new filter outperforms the traditional Unscented Kalman Filter (UKF) and Extended Kalman Filter (EKF).

scholarly journals Extraction and Filter Algorithm of Guidance Information for Full-strapdown Seeker on Rotation Missile

2018 ◽  
Vol 214 ◽  
pp. 03008 ◽  
Author(s):  
YongShan Liu ◽  
Li Song ◽  
JingLong Li
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Simple Structure ◽  
Unscented Kalman Filter ◽  
High Reliability ◽  
Engineering Application ◽  
Line Of Sight ◽  
Light Weight ◽  
Rate Information ◽  
Guidance Information

Strapdown seekers are superior to platform seekers for their simple structure, high reliability and light weight but cannot measure the line-of-sight angle rate information for the guidance of rotation missile directly. This paper aims at the engineering application of full-strapdown seekers on rotation missile problem. Firstly, a line-of-sight angle rate solution model is established. Based on the MATLAB, the extended Kalman filter (EKF) algorithm and unscented Kalman filter (UKF) algorithm are used to estimate the line-of-sight angle rate information of the full-strapdown seekers. The results show that using EKF filter and UKF filter both can obtain effective guidance information and the UKF’s effect is better.

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