Precise Orbit Determination of Global Navigation Satellite System of ...
Precise Orbit Determination of Global Navigation Satellite System of ...
Precise Orbit Determination of Global Navigation Satellite System of ...
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Chapter 6 Algorithms <strong>of</strong> <strong>Orbit</strong> <strong>Determination</strong> <strong>of</strong> IGSO, GEO and MEO <strong>Satellite</strong>s<br />
Φ<br />
kk , − = 1<br />
�<br />
1 2<br />
�<br />
�<br />
1 0 0 ∆t 0 0 ∆t<br />
0 0 a110 , a111 , Λ a1,<br />
n<br />
2<br />
�<br />
�<br />
1<br />
�<br />
2<br />
� 0 1 0 0 ∆t 0 0 ∆t<br />
0 a210 , a211 , Λ a2,<br />
n �<br />
�<br />
2<br />
�<br />
�<br />
1 2<br />
0 0 1 0 0 ∆t 0 0 ∆t<br />
a310 , a311 , Λ a �<br />
3,<br />
n<br />
�<br />
2<br />
�<br />
� 0 0 0 1 0 0 ∆t<br />
0 0 a410 , a411 , Λ a4,<br />
n �<br />
�<br />
�<br />
�<br />
0 0 0 0 1 0 0 ∆t<br />
0 a510 , a511 , Λ a5,<br />
n �<br />
� 0 0 0 0 0 1 0 0 ∆t<br />
a610 , a611 , Λ a � 6,<br />
n<br />
�<br />
�<br />
� 0 0 0 0 0 0 1 0 0 a710 , a711 , Λ a7,<br />
n �<br />
� 0 0 0 0 0 0 0 1 0 a810 , a811 , Λ a �<br />
8,<br />
n<br />
�<br />
�<br />
� 0 0 0 0 0 0 0 0 1 a910 , a911 , Λ a9,<br />
n �<br />
�<br />
0 0 0 0 0 0 0 0 0 a10,<br />
10 0 Λ 0<br />
�<br />
�<br />
�<br />
� 0 0 0 0 0 0 0 0 0 0 a11,<br />
11 Λ 0 �<br />
�<br />
�<br />
�<br />
Λ Λ Λ Λ Λ Λ Λ Λ Λ Λ Λ Λ Λ<br />
�<br />
�<br />
� 0 0 0 0 0 0 0 0 0 0 0 Λ a � nn , �<br />
where<br />
∆t = tk −tk−1 a110 , , Λ , ann are coefficients <strong>of</strong> ∆pi .<br />
The system and observation equations for Kalman filter are<br />
ϖ ϖ ϖ<br />
xk = Φk, k−1xk−1 + Γk,<br />
k−1wk� ϖ ϖ ϖ �<br />
yk = Hkxk + ε k �<br />
Then Kalman filter which can be used in kinematic orbit determination is<br />
ϖ ϖ<br />
x′ =<br />
�<br />
k k k− xk−<br />
�<br />
T<br />
P′ = +<br />
�<br />
k k k− Pk− k k−<br />
kk− Qk−<br />
kk−<br />
�<br />
− �<br />
K = P′ H H P′ H + R<br />
�<br />
ϖ ϖ ϖ<br />
�<br />
x = x′ + K y − H x′<br />
�<br />
P = I− K H P′<br />
�<br />
��<br />
T<br />
~<br />
Φ , 1 1<br />
Φ , 1 1Φ , 1 Γ , 1 1Γ , 1<br />
T<br />
T 1<br />
k k k ( k k k k )<br />
~<br />
k k k( k k k)<br />
k ( k k) k<br />
where<br />
ϖ<br />
xk ϖ<br />
wk ϖ<br />
y k<br />
ϖ<br />
ε k<br />
system status vector at epoch k<br />
process noise at epoch k<br />
vector <strong>of</strong> observations<br />
measurement noise<br />
Φ kk , −1 state transition matrix between epochs kk , −1<br />
, −1<br />
Gkk , −1 system noise matrix between epochs kk<br />
Hk design matrix<br />
Pk′ predicted covariance matrix at epoch k<br />
Pk improved covariance matrix at epoch k<br />
K k<br />
ϖ<br />
xk′ Kalman filter gain matrix<br />
prediction <strong>of</strong> ϖ ϖ~<br />
xk xk at epoch k<br />
estimation <strong>of</strong> ϖ xk based on the measurements ϖ y1 to ϖ yk−1 78<br />
(6-79)<br />
(6-80)<br />
(6-81)<br />
Because geostationary satellite moves very slowly relative to the Earth, the polynomial with second order is<br />
precise enough to be used in the state transition matrix <strong>of</strong> Eq.(6-79).