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ASEN 5070: Statistical Orbit Determination I Fall 2014 Professor Brandon A. Jones Lecture 9: Batch Estimator and Weighted LS. Announcements. Homework 3 – Due September 19 When exporting MATLAB figures, please use a high- quality image format, e.g., PNG, EPS, etc. - PowerPoint PPT Presentation
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University of ColoradoBoulder
ASEN 5070: Statistical Orbit Determination I
Fall 2014
Professor Brandon A. Jones
Lecture 9: Batch Estimator and Weighted LS
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Homework 3 – Due September 19◦ When exporting MATLAB figures, please use a high-quality
image format, e.g., PNG, EPS, etc.◦ Screen captures and JPEGs are typically not the best
option!
Lecture Quiz◦ Covers Lectures 6-8◦ Due Friday at 5pm
Friday and Next Week:◦ Probability and Statistics◦ Book Appendix A
Announcements
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Batch Estimator
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Linearized Equations
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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?
LS with Linearized System
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What can we do to estimate the state when we have observations at multiple points in time?
Observations at multiple times?
◦ 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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Reformulation for Epoch State
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Reformulation for Epoch State
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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?
The Batch Estimator
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Example Least Squares Problem
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Example Least Squares Problem
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Example Least Squares Problem
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Process all observations over a given time span in a single batch
The Batch Estimator
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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?
Shortcomings of Basic LS
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Weighted Least Squares Estimation
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For each yi, we have some weight wi
We define a set of weights
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Effects of Weights in J(x)
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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Derivation of Weighted LS Estimator
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For each yi, we have some weight wi
We define a set of weights
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Derivation of Weighted LS Estimator
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For the weighted LS estimator:
Weighted Least Squares Estimator
How do we find W ?
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What is the effect of W on the solution?
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What is the effect of W on the solution?
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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:
LS w/ A Priori Formulation
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As you will show in the homework:
LS w/ A Priori Solution
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Computation Method
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Concept Exercises
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Sent via e-mail shortly before lecture
Take a look between now and Friday◦ Feel free to work in groups!
Be ready to answer the questions at the start of lecture
The concept quiz will not be turned in for a grade
Directions