This web site will be the main tool for communicating with you. I will also use of Google Classroom.
Lectures will be given in presence. Those of you who cannot attend due to health reasons related to COVID-19 need to inform me by email. I will send you the Zoom link and the access code to enter the classroom. This is the only valid reason to attend from remote (only for the duration of the quarantine).
Luciano Iess
Giulia Nejat 2 pt
Andrea Vittori 2 pt
Mariano Conti 1 pt
Paolo Pagnozzi 1 pt
Roberto Santori 1 pt
Congratulations to the winners and all participants, whether you succeeded or not!
Winners
First place: Pasquale Tartaglia
Second place: Chiara Pazzelli, Ariele Zurria
Third place: Chiara Pozzi, Giuliano Vinci, Alessandro Beolchi
The Challenge is a valid replacement for HW1 for the students in green in this list.
Congratulations to all participants! We received many very good answers. I hope everyone learned something on OD and enjoyed solving the problem.
Luciano Iess
Color codes:
Green = pass
Red = fail
Yellow = pass, but with some errors. It is strongly advised to do the challenge, answering the first questions (1-2, TBD). The second hpmework must be a "full green".
Problem
Observable data file
Deadline: Sunday 11 April 23:59:59. The text and the observable file is also available on Google Classroom. Use Classroom to upload your solution (a pdf file) and working code. If you are not on Classroom, use email (to me and paolo.cappuccio@uniroma1.it). Be concise and go right to the point. A well done set of figures is worth a thousand words.
We will use the Zoom platform, on my NEW personal virtual room.
To access it, use the following invitation and link:
Luciano Iess is inviting you to a scheduled Zoom meeting:
Topic: Luciano Iess's Personal Meeting Room
Join Zoom Meeting:
https://uniroma1.zoom.us/j/8534489651?pwd=WEcxRWlGS0JUaG5Mb253L0NveHdvZz09
Meeting ID: 853 448 9651
Passcode: 250498
You must enter the room with your FIRST NAME and LAST NAME. You will not be admitted to the virtual classroom with nicknames.
- (5 March 2021) The Science Requirement Document of the JUICE mission is avalable in the Class Notes link (SMS, Supporting Material) filename:JUI-EST-SGS-RS-001_i2.3_SciRD.pdf
1st place: not awarded
2nd place: Fabiani, Gubernari
3rd place: Pallarés Chamorro, Di Muzio (+1 pt.)
The challenge is a valid replacement for HW1 for the following students:
Capocchiano, Di Francesca, Di Muzio, Maioli, Mattei, Mereu, Moretti, Paci, Silvestri, Sponsillo
Congratulations to all participants! Many of you did quite a good job.
Follow the link on the left panel to get the instructions for the remote exam sessions.
For detailed instructions you may also download this pdf document (last update 25/5).
The Challenge is available also on Google Classroom. The solution and working codes must be uploaded on Google Classroom by Sunday 26 April 23:59 UTC. In case of problems with Google Classroom (and only in this case), send it by email to luciano.iess@uniroma1.it AND gael.cascioli@uniroma1.it.
First place: +3 pt at the final exam
Two second places: +2 pt at the final exam
Three third places: +1 pt at the final exam
You are all encouraged to try it! There is a lot to learn. The Challenge is a valid replacement for HW 1 for students who did not pass or take it.
Click here to download the test (zip file)
The solution (a pdf file) and the source code must be uploaded by Sunday 5 April 23:59:59 CEST on Google Classroom. If you have troubles with Classroom (and only in this case), the solution may be emailed to luciano.iess@uniroma1.it AND gael.cascioli@uniroma1.it AND daniele.durante@uniroma1.it. NO EXCEPTIONS!
Suggestions and remarks: Be concise and go right to the point. Plots convey information very effectively. Including a working source code is mandatory
The link for all Google Meet classes is always the same:
https://meet.google.com/wsf-pevk-fzb
Please turn off your cameras and mute your microphones (unless you wish to ask a question). Monday's class will be again on Youtube.
The video of the exercise on Kalman filter and the Earth orbiter (19 March 2020) is available on Google Classroom for DOWNLOAD (most of you won't be able to play it directly)
https://drive.google.com/open?id=1Ooa9B57kjii7Zz6CtY5cUwo72dr3aojr&authuser=0
The video will NOT be uploaded on Youtube, at least for now.
The Matlab files are available in the Class Notes/Space Missions and Systems/Supporting Material folder.
Following the suspension of all in-class teaching activities till 15 March, we will continue the courses remotely on my Youtube streaming channel. This will enable some progress during this emergency situation.
Space Missions and Systems:
Monday, Tuesday, Wednesday 10:00-12:00
Thursday: 12:00-14:00
Space Environment:
Monday: 12:00-13:00
Tuesday: 8:00-10:00
Thursday: 10:00-12:00
To attend the classes in streaming:
Click here to access my Youtube channel.
(otherwise copy&paste the following link in your browser:
https://www.youtube.com/channel/UC03_ifjMXQt_eqvBYs3X4Kg?view_as=subscriber)
The videos are not immediately available for viewing after the streaming session is terminated. You'll have to wait about one hour grace time to be able to view it.
To ask questions, please use the chat available on the main page (give some try beforehands).
Alternatively, for more elaborate questions, give me a skype call. I'll have skype active on my laptop. My skypeid is luciano_diaa1.
Please visit this website for updates.
This is all experimental. If you have suggestions on better ways to continue the course, do not hesitate to contact me.
Luciano Iess
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Using the Matlab code and the concepts you learned during the course, answer the following questions:
1) Which observables are most sensitive to x_0?
2) Which observables are most sensitive to v_0?
3) How does the state accuracy (standatd deviation) vary with h? Make a plot or a table.
4) Try to change one of the model parameters (k_1,k_2,m). Does the filter converge? Why?
5) Implement the MVE in the Spring-Mass Matlab script.
6) How do the standard deviations of the estimated state variables change?
7) Try to simulate your own observed observables (you can find the code in the file “spring_main_batch.m”
8) Using only one type of observables at a time, what is the maximum level of noise that guarrantees convergence?
The spring-mass Matlab code has been uploaded in the folder "Class Notes"/"Space Missions and Systems"/"Supplementary Material".
The previous version is also available in the same folder. There the observable quantities are generated in the matlab code. You can play with the noise level.
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Here is the link to the recent paper on Nature and some of its echoes in the news:
Iess et al. "Measurement of Jupiter's asymmetric gravity field", Nature, 555, 220-222 (2018)
International coverage
Scientific American (really good)
The Hindustan Times (India)
Coverage in Italy
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Matlab codes for batch and sequential estimation (spring-mass system) in Class Notes - Supporting Material
Matlab code for the coherent demodulator
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For updated information you may follow me on Twitter (luciano_iess)
Seminar 1: Deep Space Navigation Systems: Where Do We Stand?
Seminar 2: The European Delta-DOR Correlator
Seminar 3: BepiColombo, the ESA Mission To Mercury; MORE: Geodesy, Geophysics, Navigation
Seminar 4 and 5: The Scientific Use of Deep Space Tracking Systems; Radio Science in Deep Space Missions
***Tour of Robledo's DNS facilities. The visit to the Robledo and Cebreros tracking complexes was an interesting and profitable experience for all of us. Follow the link to see some photos.
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An Eye on Mimas