Transcript Lecture 9
ASEN 5070: Statistical Orbit Determination I
Fall 2015
Professor Brandon A. Jones
Lecture 9: Batch Estimator and Weighted LS
University of Colorado
Boulder
Homework 3 – Due September 18
Lecture Quiz 3 Due Friday by 5pm
Future Lectures
◦ Lecture 10 – Monday 9/14 @ 4pm
◦ Lecture 11 – Monday 9/21 @ 9am
◦ Lecture 12 – Monday 9/21 @ 4pm
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Batch Estimator
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Straightforward way to estimate the state at a
time that matches the observations
What about when the observations cover
multiple points in time?
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What can we do to estimate the state x0 when we
have observations at multiple points in time?
◦ What tool(s) do we have
available to alter the
formulation?
◦ Given result from above,
how might we alter the
formulation to use a single
relationship of the form:
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Process all observations over a given time
span in a single batch
◦ The alternative sequential methods will be
discussed later
What are the shortcomings of such a
formulation?
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Process all observations over a given time
span in a single batch
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No weighting of observations
◦ How do we account for different sensors with
different accuracies?
No incorporation of previous information
◦ Known a a priori state information
◦ How do we include this in the filter?
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Weighted Least Squares Estimation
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For each yi, we have some weight wi
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Consider the case with two observations
(m=2)
If w2 > w1, which εi will have a larger
influence on J(x) ? Why?
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For the weighted LS estimator:
How do we find W ?
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Weighted Least Squares w/ A Priori
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A priori
◦ Relating to or denoting reasoning or knowledge
that proceeds from theoretical deduction rather
than from observation or experience
We have:
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As you will show in the homework:
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