Fundamentals of Kalman Filtering and Applications to GNSS
Fundamentals of Kalman Filtering and Applications to GNSS
Fundamentals of Kalman Filtering and Applications to GNSS
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Chi-Squared Statistic<br />
• Detecting anomalous sensor data<br />
– The <strong>Kalman</strong> gain matrix K P <br />
<br />
– includes the fac<strong>to</strong>r Yvk<br />
HkPk<br />
Hk<br />
R<br />
k the information<br />
matrix <strong>of</strong> innovations. The innovations are the measurement<br />
residuals v k zk<br />
Hk<br />
xˆ<br />
k , the differences between the<br />
apparent sensor outputs <strong>and</strong> predicted sensor outputs.<br />
– The associated likelihood function for innovations is<br />
1 T <br />
L vk<br />
exp<br />
vk<br />
Yvk<br />
vk<br />
,<br />
2 <br />
k<br />
– <strong>and</strong> the log-likelihood is log L vk<br />
vk<br />
Yvk<br />
vk<br />
which can<br />
be easily calculated.<br />
k<br />
H<br />
T<br />
k<br />
<br />
T<br />
<br />
1<br />
,<br />
<br />
H<br />
k<br />
P<br />
<br />
k<br />
Hk<br />
R<br />
k<br />
<br />
Y<br />
vk<br />
T<br />
,<br />
T<br />
<br />
1<br />
<strong>Kalman</strong> <strong>Filtering</strong> Consultant Associates © 2011<br />
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