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Processing of Space Monitoring Information

Approx. 14 hours to complete

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Course Summary

Learn how to process space monitoring information with this course. Develop skills in analyzing and interpreting data related to space monitoring.

Key Learning Points

Understand the basics of space monitoring and how it works
Learn how to analyze and interpret data from space monitoring
Develop skills in using software to process space monitoring information

Learning Outcomes

Ability to understand the basics of space monitoring
Develop skills in analyzing and interpreting data related to space monitoring
Ability to use software to process space monitoring information

Prerequisites or good to have knowledge before taking this course

Basic knowledge of data analysis
Familiarity with programming languages like Python
Access to a computer with internet connection

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced
Video lectures
Assignments
Quizzes

Similar Courses

Introduction to Satellite Communications
Remote Sensing and Image Analysis

Related Education Paths

Notable People in This Field

Founder and CEO, SpaceX
Professor of Space Policy, London School of Economics

Related Books

Description

Observation of near-Earth outer space is an important task for astronomers and scientists at the present time. This task is to determine the coordinate and non-coordinate characteristics of artificial space objects. According to data obtained, a catalogue of objects is created, which should be maintained and updated. For this, optical means (telescopes) can be involved. The data from these means should be processed in order to obtain information about objects in space.

In this course, we will talk about steps that should be done to detect satellites and estimate their parameters with the methods of background suppression, detection of isolated groups of bright pixels to classify them as stars or tracks. One of the items includes information about star catalogues and the astrometric reduction of the image. The examples of images in different observation conditions will be shown. Usually, during observation, a sequence of images is done and it is important to define the mutual displacement of images for suppressing the spatially correlated background. One more topic for discussion is spatiotemporal methods of filtering observation background and further extraction of spatially resolved point objects from the image.

Outline

Week 1. Image processing pipeline
Promo
Introduction
Background suppression
Median filtering
Iterative sigma clipping
Proposed filter for background suppression
Detection of isolated groups of bright pixels
Estimation of shape parameters of extracted bright areas for classification as stars or tracks
Estimation of stars and satellite tracks parameters
Astrometric reduction of the image
Star Catalogue Requirements
Determination of correspondences between detected stars and catalogue
Hashing the quads
Indexing the catalogue
Verification of hypothesis
Transformation estimation
Photometric reduction
Examples of processed images
Lecture Notes of the Course
Presentations of the Course
Literature
Test 1.1

Week 2. Correlation methods for determining the mutual displacement of images
Introduction
Problem statement
Algorithm for determining relative image shifts
Linear algorithm of shift estimation
Quadratic Algorithm of shift estimation
Literature
Programming Assignment 2.1

Week 3. Spatiotemporal methods of filtering observation background
Problem statement
Quality indicators for filtering observation background
Adaptive time filtering algorithms
Calculating the filter coefficients as an explicit shift function
Literature
Test 3.1

Week 4. Extraction of spatially resolved point objects from digital image
Problem statement
The general structure of the optimal algorithm
Detection of a single object in a fixed window
Determination of decision making statistics in the reference points of the image with a symmetric PSF, conjugated with pixel dimensions
Suboptimal algorithm of detection the objects in the reference points of an image
Examples of image sequence processing
Literature
Programming Assignment 4.1
Programming Assignment 4.2
Final Assignment

Summary of User Reviews

Discover the processing of space monitoring information with this comprehensive course on Coursera. Students found the course engaging and informative, with a high overall rating. Many users appreciated the real-world examples and practical applications of the course materials.

Key Aspect Users Liked About This Course

Real-world examples and practical applications

Pros from User Reviews

Engaging and informative course materials
Real-world examples and practical applications
In-depth coverage of space monitoring information
Clear and concise explanations
Expert instructors

Cons from User Reviews

Some users found the course challenging
Course materials may be outdated
Lack of interaction with instructors
Limited opportunities for hands-on learning
Course may be too technical for some users

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