cs401r_w2016:lab7
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cs401r_w2016:lab7 [2016/02/17 15:31] admin created |
cs401r_w2016:lab7 [2021/06/30 23:42] |
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| - | ====Objective:==== | ||
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| - | To understand state space models, and in particular, the Kalman filter. | ||
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| - | ---- | ||
| - | ====Deliverable:==== | ||
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| - | For this lab, you will implement the Kalman filter algorithm on a custon dataset. Your notebook should produce a visualization of the observations, the true state, the estimated state, and your estimate of the variance of your state estimates. | ||
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| - | ---- | ||
| - | ====Grading:==== | ||
| - | Your notebook will be graded on the following elements: | ||
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| - | * 10% Data is correctly mean-centered | ||
| - | * 20% Correctly updates responsibilities | ||
| - | * 20% Correctly updates means | ||
| - | * 20% Correctly updates covariances | ||
| - | * 20% Correctly updates mixing weights | ||
| - | * 10% Final plot is tidy and legible | ||
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| - | ---- | ||
| - | ====Description:==== | ||
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| - | For this lab, you will perform simple target tracking using the random accelerations model. The model is therefore given by | ||
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| - | <code python> | ||
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| - | # the dynamics model | ||
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| - | # the observation model | ||
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| - | # the dynamics noise | ||
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| - | # the observation noise | ||
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| - | # the initial state | ||
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| - | mu_t = | ||
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| - | </code> | ||
cs401r_w2016/lab7.txt ยท Last modified: 2021/06/30 23:42 (external edit)
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