Propagate the state and uncertainty without process noise for two revolutions with the mean of: 153446.180 m and covariance of: 41874155.872 m 0 m μ 3066.875 m/s -11.374 m/s 0 m/s 6494.080 -376.139 0 -376.139 22.560 0 0.0160 (-9.883-10-4) -0.494 0 0.0286 0 0 0 1.205 0 0 (-6.071-10-5) 0.0160 (-9.883-10-4) 0 -0.494 0.0286 0 0 0 (-6.071-10-5) 0 (4.437-108) (-1.212-10-6) (-1.212-106) (3.762-10-5) 0 0 0 (3.390-10-9) (a) First propagate using a Monte Carlo process where you draw samples at the initial time and propagate all samples. Compute the mean and covariance from the samples at the final time. How many samples should you draw for an accurate representation; comment on how many you did draw (you might draw a smaller number of samples, but at least 100).
Propagate the state and uncertainty without process noise for two revolutions with the mean of: 153446.180 m and covariance of: 41874155.872 m 0 m μ 3066.875 m/s -11.374 m/s 0 m/s 6494.080 -376.139 0 -376.139 22.560 0 0.0160 (-9.883-10-4) -0.494 0 0.0286 0 0 0 1.205 0 0 (-6.071-10-5) 0.0160 (-9.883-10-4) 0 -0.494 0.0286 0 0 0 (-6.071-10-5) 0 (4.437-108) (-1.212-10-6) (-1.212-106) (3.762-10-5) 0 0 0 (3.390-10-9) (a) First propagate using a Monte Carlo process where you draw samples at the initial time and propagate all samples. Compute the mean and covariance from the samples at the final time. How many samples should you draw for an accurate representation; comment on how many you did draw (you might draw a smaller number of samples, but at least 100).
Elements Of Electromagnetics
7th Edition
ISBN:9780190698614
Author:Sadiku, Matthew N. O.
Publisher:Sadiku, Matthew N. O.
ChapterMA: Math Assessment
Section: Chapter Questions
Problem 1.1MA
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Can you help me solve this in MATLAB?
Transcribed Image Text:Propagate the state and uncertainty without process noise for two revolutions with the mean
of:
153446.180 m
and covariance of:
41874155.872 m
0 m
μ
3066.875 m/s
-11.374 m/s
0 m/s
6494.080
-376.139
0
-376.139
22.560
0
0.0160
(-9.883-10-4)
-0.494
0
0.0286
0
0
0
1.205
0
0
(-6.071-10-5)
0.0160
(-9.883-10-4)
0
-0.494
0.0286
0
0
0
(-6.071-10-5)
0
(4.437-108) (-1.212-10-6)
(-1.212-106) (3.762-10-5)
0
0
0
(3.390-10-9)
(a) First propagate using a Monte Carlo process where you draw samples at the initial time
and propagate all samples. Compute the mean and covariance from the samples at the
final time. How many samples should you draw for an accurate representation; comment
on how many you did draw (you might draw a smaller number of samples, but at least
100).
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