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David Silver,
Stephen Welch,
Abdullah Zaidi,
Andreas Haja,
and
Aaron Brown
Register for this course and see more details by visiting:
OpenCourser.com/course/aqjgu6/kalman
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What's inside
Syllabus
Meet the team at Mercedes who will help you track objects in real-time with Sensor Fusion.
Learn from the best! Sebastian Thrun will walk you through the usage and concepts of a Kalman Filter using Python.
Read more
Syllabus
Meet the team at Mercedes who will help you track objects in real-time with Sensor Fusion.
Learn from the best! Sebastian Thrun will walk you through the usage and concepts of a Kalman Filter using Python.
Read more
In this lesson, you'll build a Kalman Filter in C++ that's capable of handling data from multiple sources. Why C++? Its performance enables the application of object tracking with a Kalman Filter in real-time.
While Extended Kalman Filters work great for linear motion, real objects rarely move linearly. With Unscented Kalman Filters, you'll be able to accurately track non-linear motion!
In this lesson, students will submit the project that they have developed over the previous lessons.
This optional content is designed to refresh your knowledge of trigonometry and geometry.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Employs the preferred tools and methodologies of industry leaders like Mercedes in live object tracking
Instructed by Sebastian Thrun, a highly acclaimed specialist in object tracking using Kalman Filters
Provides practical training in applying Kalman Filters in C++, which is essential for real-time object tracking
Covers Unscented Kalman Filters, a powerful tool for tracking non-linear motion patterns
Students get hands-on experience by completing a project covering multiple lessons
Save this course
Save Kalman Filters to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Kalman Filters with these
activities:
Connect with Experts in Object Tracking
Show steps
Help you establish connections with experts in the field, providing access to valuable insights, mentorship, and support throughout your learning journey.
Browse courses on
Object Tracking
Show steps
-
Attend conferences, workshops, or online events related to object tracking.
-
Reach out to professors, researchers, or industry professionals in the field.
Recall Linear Motion and Kalman Filtering
Show steps
Help you refresh your knowledge of linear motion and Kalman Filtering before the course begins, making it easier to follow along with the lessons.
Show steps
-
Review your notes or textbook chapters on linear motion and Kalman Filtering.
-
Take a practice quiz or exam to test your understanding.
Discussion Group for Object Tracking
Show steps
Provide a platform for discussing and exchanging ideas with peers, fostering collaboration and a deeper understanding of object tracking concepts.
Browse courses on
Object Tracking
Show steps
-
Join or start a discussion group focused on object tracking.
-
Participate in discussions, asking questions, sharing insights, and responding to others.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Interactive Tutorials for Object Tracking with Sensor Fusion
Show steps
Provide hands-on practice and reinforce the concepts covered in the course by following guided tutorials on object tracking with sensor fusion.
Browse courses on
Object Tracking
Show steps
-
Find interactive tutorials on object tracking and sensor fusion.
-
Work through the tutorials, completing the exercises and challenges.
Develop a Kalman Filter in Python
Show steps
Challenge you to apply your knowledge by building a Kalman Filter in Python, solidifying your understanding of its implementation and functionality.
Browse courses on
Kalman Filter
Show steps
-
Gather the necessary resources and materials for building a Kalman Filter in Python.
-
Code and test the Kalman Filter, ensuring its accuracy and efficiency.
Practice Problems on Extended Kalman Filters
Show steps
Solidify your understanding of Extended Kalman Filters by solving practice problems, improving your ability to apply the concepts to real-world scenarios.
Browse courses on
Extended Kalman Filters
Show steps
-
Find practice problems on Extended Kalman Filters online or in textbooks.
-
Solve the problems, checking your answers against provided solutions or online resources.
Contribute to Open-Source Projects on Object Tracking
Show steps
Give you hands-on experience and deepen your understanding by contributing to open-source projects related to object tracking, immersing you in the community of practice.
Browse courses on
Object Tracking
Show steps
-
Identify open-source projects on object tracking that align with your interests.
-
Review the project documentation and codebase.
-
Identify areas where you can contribute, such as bug fixes, feature enhancements, or documentation improvements.
Write a Summary of Advanced Object Tracking Techniques
Show steps
Encourage you to synthesize and share your understanding by writing a summary of advanced object tracking techniques, reinforcing your knowledge and contributing to the body of knowledge.
Browse courses on
Object Tracking
Show steps
-
Research and gather information on advanced object tracking techniques.
-
Organize and outline the content of your summary.
Connect with Experts in Object Tracking
Show steps
Help you establish connections with experts in the field, providing access to valuable insights, mentorship, and support throughout your learning journey.
Browse courses on
Object Tracking
Show steps
Show steps
Show steps
- Attend conferences, workshops, or online events related to object tracking.
- Reach out to professors, researchers, or industry professionals in the field.
Recall Linear Motion and Kalman Filtering
Show steps
Help you refresh your knowledge of linear motion and Kalman Filtering before the course begins, making it easier to follow along with the lessons.
Show steps
Show steps
Show steps
- Review your notes or textbook chapters on linear motion and Kalman Filtering.
- Take a practice quiz or exam to test your understanding.
Discussion Group for Object Tracking
Show steps
Provide a platform for discussing and exchanging ideas with peers, fostering collaboration and a deeper understanding of object tracking concepts.
Browse courses on
Object Tracking
Show steps
Show steps
Show steps
- Join or start a discussion group focused on object tracking.
- Participate in discussions, asking questions, sharing insights, and responding to others.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Interactive Tutorials for Object Tracking with Sensor Fusion
Show steps
Provide hands-on practice and reinforce the concepts covered in the course by following guided tutorials on object tracking with sensor fusion.
Browse courses on
Object Tracking
Show steps
Show steps
Show steps
- Find interactive tutorials on object tracking and sensor fusion.
- Work through the tutorials, completing the exercises and challenges.
Develop a Kalman Filter in Python
Show steps
Challenge you to apply your knowledge by building a Kalman Filter in Python, solidifying your understanding of its implementation and functionality.
Browse courses on
Kalman Filter
Show steps
Show steps
Show steps
- Gather the necessary resources and materials for building a Kalman Filter in Python.
- Code and test the Kalman Filter, ensuring its accuracy and efficiency.
Practice Problems on Extended Kalman Filters
Show steps
Solidify your understanding of Extended Kalman Filters by solving practice problems, improving your ability to apply the concepts to real-world scenarios.
Browse courses on
Extended Kalman Filters
Show steps
Show steps
Show steps
- Find practice problems on Extended Kalman Filters online or in textbooks.
- Solve the problems, checking your answers against provided solutions or online resources.
Contribute to Open-Source Projects on Object Tracking
Show steps
Give you hands-on experience and deepen your understanding by contributing to open-source projects related to object tracking, immersing you in the community of practice.
Browse courses on
Object Tracking
Show steps
Show steps
Show steps
- Identify open-source projects on object tracking that align with your interests.
- Review the project documentation and codebase.
- Identify areas where you can contribute, such as bug fixes, feature enhancements, or documentation improvements.
Write a Summary of Advanced Object Tracking Techniques
Show steps
Encourage you to synthesize and share your understanding by writing a summary of advanced object tracking techniques, reinforcing your knowledge and contributing to the body of knowledge.
Browse courses on
Object Tracking
Show steps
Show steps
Show steps
- Research and gather information on advanced object tracking techniques.
- Organize and outline the content of your summary.
Career center
Learners who complete Kalman Filters will develop knowledge and skills
that may be useful to these careers:
Robotics Engineer
Robotics Engineer
A Robotics Engineer designs, builds, and tests robots. Kalman Filters are widely used in robotics for navigation, motion planning, and control. This course will be especially helpful for Robotics Engineers working on autonomous robots and mobile robotics.
Control Systems Engineer
Control Systems Engineer
A Control Systems Engineer designs, develops, and implements control systems for a variety of applications. Kalman Filters are widely used in navigation, guidance, and control systems. This course can help Control Systems Engineers create robust and reliable control systems. Control Systems Engineers working with robotics and autonomous systems will find this course to be especially helpful.
Financial Analyst
Financial Analyst
A Financial Analyst provides and interprets financial advice to clients. Kalman Filters can be used to track financial data over time and identify trends. Furthermore, Kalman Filters can be used to build financial models and predict future financial outcomes. This course would be especially helpful for Financial Analysts working on trading, investment, and financial planning.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and analytical techniques to solve complex problems in business and industry. Kalman Filters are used in operations research to solve problems in areas such as inventory management, scheduling, and queueing theory. This course will be helpful for Operations Research Analysts working on optimization and simulation projects.
Systems Engineer
Systems Engineer
A Systems Engineer designs, develops, and integrates complex systems. Kalman Filters are used in systems engineering to solve problems in areas such as system modeling, simulation, and control. This course would be helpful for Systems Engineers working on autonomous systems, real-time control systems, and embedded systems.
Data Scientist
Data Scientist
A Data Scientist mines, processes, and interprets large datasets to develop predictive models and make recommendations. Kalman Filters are one of the most widely used state space models, and as such, can be very useful for Data Scientists. In particular, Data Scientists working on time series analysis, filtering and smoothing of real time data, and non-linear data will find this course especially helpful.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and tests software applications. Kalman Filters are used in software engineering to solve problems in areas such as computer vision, sensor fusion, and robot motion planning. This course would be especially helpful for Software Engineers working on autonomous systems, real-time control systems, and embedded systems.
Mechanical Engineer
Mechanical Engineer
A Mechanical Engineer designs and builds machines and other mechanical systems. Kalman Filters are used in a variety of mechanical engineering applications, including robotics, navigation, and control systems. This course will be especially helpful for Mechanical Engineers working on autonomous systems and real-time control systems.
Aerospace Engineer
Aerospace Engineer
An Aerospace Engineer designs and develops aircraft, spacecraft, and other related systems. A Kalman Filter course is especially relevant to Aerospace Engineers because understanding real-time, multiple source data is important to ensure the precise tracking and control of aircraft and spacecraft. For Aerospace Engineers who wish to work in navigation, guidance, and control systems, this course will be especially helpful in that it can help them develop specific problem-solving skills.
Transportation Engineer
Transportation Engineer
A Transportation Engineer designs and manages transportation systems. Kalman Filters are used in transportation engineering to solve problems in areas such as traffic modeling, simulation, and control. This course would be especially helpful for Transportation Engineers working on intelligent transportation systems and autonomous vehicles.
Geophysicist
Geophysicist
A Geophysicist studies the physical properties of the Earth and its atmosphere using techniques from the physical sciences. Kalman Filters are used to track and analyze geophysical data from a variety of sources, including seismic data and data from remote sensing satellites. This course may help Geophysicists develop data analysis tools and pipelines, and will help them stay at the forefront of geophysical exploration and research.
Mechatronics Engineer
Mechatronics Engineer
A Mechatronics Engineer designs and builds electromechanical systems that combine mechanical, electrical, and computer engineering. Kalman Filters are used in mechatronics to perform tasks such as state estimation, control, and navigation. This course would be helpful to Mechatronics Engineers working on autonomous vehicles, robotics, and other mechatronic systems.
Data Analyst
Data Analyst
A Data Analyst cleans, prepares, and analyzes data to derive meaningful insights. This course would be helpful because one common use of Kalman Filters is in time series analysis. Time series analysis is an important component of data analysis, especially in domains where the order or timing of data is important.
Electrical Engineer
Electrical Engineer
An Electrical Engineer designs and develops electrical and electronic systems. Kalman Filters are widely used in electrical engineering, especially in power systems and signal processing. This course will be especially helpful for Electrical Engineers working on embedded systems that make use of tracking, navigation, and control systems.
Biostatistician
Biostatistician
A Biostatistician applies statistical methods to biological data to answer questions and make inferences about public health and medical issues. Kalman Filters are applied to biological data to track changes over time. Many biological processes exhibit non-linear behavior. This course covers Kalman Filters, including the Unscented Kalman Filter, which is useful for tracking non-linear motion. Thus, this course may be helpful to Biostatisticians.
For more career information including salaries, visit:
OpenCourser.com/course/aqjgu6/kalman
Reading list
We've selected four books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Kalman Filters.
Provides a thorough introduction to the theory of optimal state estimation and its applications to a wide range of engineering problems. It valuable resource for students and practitioners who want to learn more about the Kalman filter and other optimal state estimation techniques.
Provides a comprehensive treatment of Kalman filtering and time series analysis. It valuable resource for students and practitioners who want to learn more about these topics.
Provides a comprehensive treatment of nonlinear filtering. It valuable resource for students and practitioners who want to learn more about this topic.
Provides a gentle introduction to the Kalman filter for beginners. It valuable resource for students and practitioners who want to learn more about this topic.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/aqjgu6/kalman
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Time
40320 hours
Level
Advanced
Via
Udacity
Instructors
David Silver
Stephen Welch
Abdullah Zaidi
Andreas Haja
Aaron Brown
Language
English
Good to know
Employs the preferred tools and methodologies of industry leaders like Mercedes in live object tracking
Instructed by Sebastian Thrun, a highly acclaimed specialist in object tracking using Kalman Filters
Provides practical training in applying Kalman Filters in C++, which is essential for real-time object tracking
Covers Unscented Kalman Filters, a powerful tool for tracking non-linear motion patterns
Students get hands-on experience by completing a project covering multiple lessons
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