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Introduction to Kalman filter with Python
Rating: 3.6 out of 5(44 ratings)
202 students

Introduction to Kalman filter with Python

Introduction to Kalman filter for beginners
Created byMustafa Gebril
Last updated 4/2024
English
English [Auto],

What you'll learn

  • Learn Kalma filter in practical way
  • No need of heavily math
  • Apply Kalman filter for uncertainty reduction
  • Apply both one dimensional and Multi-dimensional Kalman filter
  • Use Kalman filter for sensor fusion

Course content

3 sections • 12 lectures • 50m total length

  • Table of contents2:27

    Explore the Kalman filter from a simple moving-average start to a practical, multi-dimensional solution in Python, and apply it to sensor fusion through clear, implementable steps.

  • Moving Average Filter7:17

    Learn how moving average filters reduce measurement noise and uncertainty as a step toward Kalman filtering, balancing new measurements with prior predictions to improve localization.

  • Moving Average Filter Python Code3:52

    Explore how a moving average filter smooths a random zero-mean, unit-variance signal in Python by updating the average with each new sample, reducing noise over time.

  • g Filter5:32

    Explore the Kalman filter, compare it with moving-average filters, and learn how it balances prediction and measurement to produce a robust, weighted estimate that reduces noise.

  • g Filter Python code2:58

    Explore the Kalman filter implementation in Python, comparing prediction and measurement updates on a 1000-sample signal with zero-mean, unit-variance noise.

  • g-h Filter4:01

    Explore the g-h filter within a python workflow, filtering a high-noise signal through prediction and measurement updates to produce a clearer trajectory.

Requirements

  • Basics of python

Description

Kalman filter is used for variety of engineering problems, yet moth of available resources are heavily mathematics, and hard to be understood. Here we will study Kalman filter with minimum math, only the math needed for implementation. With lots of practical python code. The course includes advanced topics like sensor fusion. All in practical intuitive wave, with no math hustle. 

Who this course is for:

  • Engineering Students

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