Data Fusion with Linear Kalman Filter

  • Category Other
  • Type Tutorials
  • Language English
  • Total size 1.7 GB
  • Uploaded By freecoursewb
  • Downloads 164
  • Last checked 3 days ago
  • Date uploaded 4 years ago
  • Seeders 3
  • Leechers 3

Infohash : 645873897553C7899DBC6DDBD445B1209BFCFE60



Data Fusion with Linear Kalman Filter

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.70 GB | Duration: 5h 25m
What you'll learn
How to probabilistically express uncertainty using probability distributions
How to convert differential systems into a state space representation
How to simulate and describe state space dynamic systems
How to use Least Squares Estimation to solve estimation problems
How to use the Linear Kalman Filter to solve optimal estimation problems
How to derive the system matrices for the Kalman Filter in general for any problem
How to optimally tune the Linear Kalman Filter for best performance
How to implement the Linear Kalman Filter in Python

Requirements
Basic understanding of linear algebra
Basic Python Programming Experience

Description
You need to learn know Data Fusion and Kalman Filtering!

The Kalman filter is one of the greatest discoveries in the history of estimation and data fusion theory, and perhaps one of the greatest engineering discoveries in the twentieth century. It has enabled mankind to do and build many things which could not be possible otherwise. It has immediate application in control of complex dynamic systems such as cars, aircraft, ships and spacecraft.

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Files:

[ FreeCourseWeb.com ] Udemy - Data Fusion with Linear Kalman Filter
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1. Welcome
    • 1. Welcome to the Course-en_US.srt (3.4 KB)
    • 1. Welcome to the Course.mp4 (17.6 MB)
    • 2. Course Outline-en_US.srt (1.1 KB)
    • 2. Course Outline.mp4 (3.3 MB)
    • 3. Setting Up Python-en_US.srt (4.1 KB)
    • 3. Setting Up Python.mp4 (19.3 MB)
    • Linear Kalman Filter Notes and Summary.html (0.2 KB)
    2. Introduction
    • 1. What is Data Fusion-en_US.srt (5.8 KB)
    • 1. What is Data Fusion.mp4 (25.7 MB)
    • 2. How does Sensor Fusion Work-en_US.srt (7.2 KB)
    • 2. How does Sensor Fusion Work.mp4 (31.6 MB)
    • 3. Learning Roadmap-en_US.srt (1.7 KB)
    • 3. Learning Roadmap.mp4 (4.0 MB)
    • Linear Kalman Filter Notes and Summary.html (0.2 KB)
    3. Probability
    • 1. Basic Probability-en_US.srt (7.0 KB)
    • 1. Basic Probability.mp4 (30.1 MB)
    • 10. Gaussian Probability Distribution-en_US.srt (6.8 KB)
    • 10. Gaussian Probability Distribution.mp4 (16.6 MB)
    • 11. Linear Transformation of Gaussian Distribution-en_US.srt (7.4 KB)
    • 11. Linear Transformation of Gaussian Distribution.mp4 (28.5 MB)
    • 12. Multiple Random Variables-en_US.srt (15.9 KB)
    • 12. Multiple Random Variables.mp4 (47.5 MB)
    • 13. Mutltvariate Statistics-en_US.srt (6.6 KB)
    • 13. Mutltvariate Statistics.mp4 (21.7 MB)
    • 14. Multivariate Gaussian Distribution-en_US.srt (4.0 KB)
    • 14. Multivariate Gaussian Distribution.mp4 (13.2 MB)
    • 15. Linear Transformation of Uncertainities-en_US.srt (5.8 KB)
    • 15. Linear Transformation of Uncertainities.mp4 (19.6 MB)
    • 2. Mutual Exclusivity-en_US.srt (7.4 KB)
    • 2. Mutual Exclusivity.mp4 (36.4 MB)
    • 3. Conditional Probability-en_US.srt (10.4 KB)
    • 3. Conditional Probability.mp4 (37.5 MB)
    • 4. Bayes Theorem-en_US.srt (6.1 KB)
    • 4. Bayes Theorem.mp4 (19.6 MB)
    • 5. Random Variables-en_US.srt (4.1 KB)
    • 5. Random Variables.mp4 (15.5 MB)
    • 6. Probability Density Functions-en_US.srt (10.7 KB)
    • 6. Probability Density Functions.mp4 (30.3 MB)
    • 7. Expectation Operator-en_US.srt (6.6 KB)
    • 7. Expectation Operator.mp4 (18.5 MB)
    • 8. Distribution Statistical Properties-en_US.srt (5.6 KB)
    • 8. Distribution Statistical Properties.mp4 (14.3 MB)
    • 9. Uniform Probability Distribution-en_US.srt (3.0 KB)
    • 9. Uniform Probability Distribution.mp4 (8.0 MB)
    • Linear Kalman Filter Notes and Summary.html (0.2 KB)
    • Probability Notes and Summary.html (1.1 KB)
    4. Dynamic Systems
    • 1. Differential Equations-en_US.srt (5.1 KB)
    • 1. Differential Equations.mp4 (82.3 MB)
    • 2. State Space Representation-en_US.srt (6.0 KB)
    • 2. State Space Representation.mp4 (22.1 MB)
    • 3. Differential Equations and State Space-en_US.srt (4.7 KB)
    • 3. Differential Equations and State Space.mp4 (16.2 MB)
    • 4. Continuous and Discrete Time-en_US.srt (5.3 KB)
    • 4. Continuous and Discrete Time.mp4 (18.5 MB)
    • 5. Mathematical Models of Dynamic Systems-en_US.srt (8.7 KB)
    • 5. Mathematical Models of Dynamic Systems.mp4 (26.2 MB)
    • 6. Continuous to Discrete Model Conversions-en_US.srt (9.4 KB)
    • 6. Continuous to Discrete Model Conversions.mp4 (24.6 MB)
    • 7. Simulation of Models-en_US.srt (20.8 KB)
    • 7. Simulation of Models.mp4 (65.4 MB)
    • Dynamic System Notes and Summary.html (0.1 KB)
    • Linear Kalman Filter Notes and Summary.html (0.2 KB)
    • Python Simulation Exercise.html (1.9 KB)
    5. Least Squares Estimation
    • 1. Estimation of a Constant-en_US.srt (15.8 KB)
    • 1. Estimation of a Constant.mp4 (39.8 MB)
    • 2. Weighted Least Squares-en_US.srt (14.5 KB)
    • 2. Weighted Least Squares.mp4 (38.8 MB)
    • 3. Recursive Least Squares-en_US.srt (17.6 KB)
    • 3. Recursive Least Squares.mp4 (45.0 MB)
    • 5.1-Least-Squares-Estimation.pdf (87.3 KB)
    • 5.2-Weighted-Least-Squares-Estimation.pdf (83.4 KB)
    • 5.3-Recursive-Least-Squares-Estimation.pdf (76.5 KB)
    • Least Squares Estimation Summary.html (0.1 KB)
    • Linear Kalman Filter Notes and Summary.html (0.2 KB)
    6. Linear Kalman Filter
    • 1. What is the Kalman Filter-en_US.srt (5.1 KB)
    • 1. What is the Kalman Filter.mp4 (16.6 MB)
    • 10. 2D Tracker Update Step-en_US.srt (6.3 KB)
    • 10. 2D Tracker Update Step.mp4 (17.8 MB)
    • 11. 2D Tracker Update Step Explaination-en_US.srt (15.2 KB)
    • 11. 2D Tracker Update Step Explaination.mp4 (61.5 MB)
    • 12. 2D Tracker Initial Conditions-en_US.srt (5.9 KB)
    • 12. 2D Tracker Initial Conditions.mp4 (19.2 MB)
    • 13. 2D Tracker Initial Condition Explaination-en_US.srt (8.9 KB)
    • 13. 2D Tracker Initial Condition Explaination.mp4 (40.9 MB)
    • 14. Kalman Filter Tuning-en_US.srt (14.8 KB)
    • 14. Kalman Filter Tuning.mp4 (44.3 MB)
    • 15. 2D Tracker Filter Tuning-en_US.srt (24.2 KB)
    • 15. 2D Tracker Filter Tuning.mp4 (132.4 MB)
    • 16. Implementation Notes-en_US.srt (6.3 KB)
    • 16. Implementation Notes.mp4 (21.8 MB)
    • 2. Types of Kalman Filters-en_US.srt (3.4 KB)
    • 2. Types of Kalman Filters.mp4 (12.0 MB)
    • 3. How Does the Linear Kalman Filter Work-en_US.srt (9.7 KB)
    • 3. How Does the Linear Kalman Filter Work.mp4 (110.8 MB)
    • 4. 2D Tracker Example Problem Overview-en_US.srt (12.1 KB)
    • 4. 2D Tracker Example Problem Overview.mp4 (53.1 MB)
    • 5. 2D Tracker Process Model-en_US.srt (10.2 KB)
    • 5. 2D Tracker Process Model.mp4 (29.9 MB)
    • 6. Kalman Filter Prediction Step-en_US.srt (6.3 KB)
    • 6. Kalman Filter Prediction Step.mp4 (24.9 MB)
    • 7. 2D Tracker Prediction Step-en_US.srt (12.8 KB)
    • 7. 2D Tracker Prediction Step.mp4 (41.8 MB)
    • 8. 2D Tracker Prediction Step Explaination-en_US.srt (16.1 KB)
    • 8. 2D Tracker Prediction Step Explaination.mp4 (61.0 MB)
    • 9. Kalman Filter Update Step-en_US.srt (11.7 KB)
    • 9. Kalman Filter Update Step.mp4 (41.3 MB)
    • Linear Kalman Filter Notes and Summary.html (0.2 KB)
    • assignment1_answer.py (0.7 KB)
    • assignment1_initial_conditions.py (3.1 KB)
    • assignment1_initial_conditions_answer.py (3.3 KB)
    • assignment1_prediction.py (1.6 KB)
    • assignment1_prediction_answer.py (1.8 KB)
    • assignment1_update.py (3.0 KB)
    • assignment1_update_answer.py (3.0 KB)
      • kfsims
      • __init__.py (0.0 KB)
      • kfmodels.py (0.7 KB)
      • kftracker2d.py (2.6 KB)
      • tracker2d.py (12.4 KB)
      • vehiclemodel2d.py (1.0 KB)
      7. Pendulum Example
      • 1. Pendulum Estimation Problem-en_US.srt (2.3 KB)
      • 1. Pendulum Estimation Problem.mp4 (13.3 MB)
      • 2. System and Measurement Dynamics-en_US.srt (7.3 KB)
      • 2. System and Measurement Dynamics.mp4 (27.3 MB)
      • 3. Kalman Filter Model Implementation-en_US.srt (9.5 KB)
      • 3. Kalman Filter Model Implementation.mp4 (37.5 MB)
      • 4. Kalman Filter Performance and Tuning-en_US.srt (7.8 KB)
      • 4. Kalman Filter Performance and Tuning.mp4 (56.3 MB)
      • 5. Summary-en_US.srt (1.4 KB)
      • 5. Summary.mp4 (6.2 MB)
      • Linear Kalman Filter Notes and Summary.html (0.2 KB)
      • assignment2_answer.py (0.6 KB)
      • assignment2_filter.py (3.2 KB)
      • assignment2_filter_answer.py (3.3 KB)
      • assignment2_sims.py (1.8 KB)
        • kfsims
        • __init__.py (0.0 KB)
        • kfmodels.py (0.7 KB)
        • kfpendulum.py (2.8 KB)
        • pendulum.py (8.3 KB)
        • pendulummodel.py (0.8 KB)
        8. Conculsion
        • 1. Kalman Filter Summary-en_US.srt (3.2 KB)
        • 1. Kalman Filter Summary.mp4 (13.3 MB)
        • Data-Fusion-with-the-Linear-Kalman-Filter-Slides.pdf (22.5 MB)
        • Linear Kalman Filter Notes and Summary.html (0.2 KB)
        • Bonus Resources.txt (0.3 KB)

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