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Extended Kalman Filter for Orbit Determination

version 1.0.0 (5.28 MB) by Ozan Kilic
Extended Kalman Filter Algorithm for Kinematic Orbit Determination using GPS Observations

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Updated 16 Nov 2019

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The advances in the Global Navigation Satellite Systems, the increase in its geometric strength and the availability of high number of observations in receivers onboard LEO satellite make it possible to use a pure geometric approach for orbit determination.

Due to its recursive structure, Kalman Filter is widely applied in orbit determination. The Extended Kalman Filter (EKF) provides an efficient linearization performance. Thus, it is one of the most preferred algorithms for nonlinear estimation problems. Unlike the linearized version of classical Kalman Filter equations, EKF makes use of estimated state of each epoch for linearization. This allows state estimation to be more accurate.

- RINEX observation file from GRACE (The Gravity Recovery and Climate Experiment mission) satellite is used as test data.
- Only code pseudoranges are used for the calculation
- GPS satellite positions are obtained from IGS Final products (.sp3).
- In order to deal with outliers in the observations, 3-sigma rule is applied in each observation epoch.
- Due to kinematic structure, Dynamic matrix F for 8 component (x,y,z,vx,vy,vz,cb,cd) is defined as:

F = [ 0 0 0 1 0 0 0 0;
0 0 0 0 1 0 0 0;
0 0 0 0 0 1 0 0;
0 0 0 0 0 0 0 0;
0 0 0 0 0 0 0 0;
0 0 0 0 0 0 0 0;
0 0 0 0 0 0 0 1;
0 0 0 0 0 0 0 0];

Cite As

Ozan Kilic (2019). Extended Kalman Filter for Orbit Determination (https://www.mathworks.com/matlabcentral/fileexchange/73370-extended-kalman-filter-for-orbit-determination), MATLAB Central File Exchange. Retrieved .

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MATLAB Release Compatibility
Created with R2018a
Compatible with any release
Platform Compatibility
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