How to Minimize Error in Elliptical Orbit Model using Linear Algebra

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SUMMARY

The discussion focuses on minimizing error in an elliptical orbit model using linear algebra techniques. The constraint equation is given as x1r² + x2s² + x3rs = 1, with noisy measurements provided for the coordinates (r, s). The system of equations is represented as Ax + b, where A is a 10x3 matrix and b is a 10x1 vector. Three MATLAB methods for obtaining the least-square-error solution are discussed: x = A\b, x = pinv(A)*b, and x = inv(A'*A)*A'*b, with an emphasis on plotting the resulting ellipse.

PREREQUISITES
  • Understanding of linear algebra concepts, particularly least squares.
  • Familiarity with MATLAB syntax and matrix operations.
  • Knowledge of elliptical orbit equations and their representation in rectangular coordinates.
  • Basic skills in data fitting and error minimization techniques.
NEXT STEPS
  • Research "MATLAB least squares fitting" to understand implementation details.
  • Explore "Regularization techniques in linear algebra" for handling inconsistent systems.
  • Learn about "Elliptical orbit equations" and their applications in physics.
  • Investigate "Plotting in MATLAB" to visualize the fitted ellipse based on computed parameters.
USEFUL FOR

Students in mathematics or physics, data analysts, and anyone involved in modeling elliptical orbits or fitting conic sections to noisy data.

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Linear Algebra Homework help!

Homework Statement


Suppose a particular object is modeled as moving in an elliptical orbit centered at
the origin. Its nominal trajectory is described in rectangular coordinates (r;s) by the
constraint equation x1r^2 +x2s^2 +x3rs = 1, where x1; x2; and x3 are unknown parameters that specify the orbit. We have available the following noisy measurements of
the object’s coordinates (r;s) at ten different points on its orbit:

(0:6925;0:0592) (0:3582;0:4110) (0:2514;0:3763) (0:0764;0:5453)
(0:4249;0:3768) (0:6917;0:0252) (0:3831;0:2116) (0:0027;0:3801)
(0:0865;0:3628) (0:5428;0:2889)
Using the assumed constraint equation, arrange the given information in the form of
the linear system of equation Ax + b, where A is a known 10x3 matrix, b is a known
10 x1 vector, and x = (x1; x2; x3)^T.
This system of 10 equations in 3 unknowns is inconsistent. We wish to find the solution x that minimizes the Euclidean norm of error Ax +b. Compare the solution obtained by using the following MATLAB invocations, each of which in principle gives the desired least-square-error solution:
(a). x = A\b
(b). x = pinv(A) *b
(c). x = inv(A'*A)*A'*b

Plot the ellipse that corresponds to your estimate of x. Attach the m-fil

I am way over my head guys! please help a girl out!
 
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Welcome to PF;
It helps to give it a shot and tell us about it.
You should have some examples of this sort of thing in your notes and your coursework to date so you do have someplace to start from. Show your reasoning until you get stuck and we'll be able to help where you get unstuck.

BTW: there are lots of ways to fit conics to noisy data ... when you work through from your notes, we'll be tipped off about which one you are supposed to be using.
So far it looks like a regularization problem.
 

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