From lunar landers to robot arms, Kalman Filters are the mathematical backbone of modern control and estimation. But most engineers never move beyond the formula.
This course makes the Kalman Filter real. You’ll build intuition from the ground up—starting with simple moving averages and advancing to full state estimation, modeling, and sensor fusion across multiple platforms.
By the end, you’ll understand the “why” behind the math, not just the syntax. And you’ll implement it yourself—in both Python and C++, including Arduino deployment for filtering noisy sensor data on real robot joints.
✅ What You’ll Build
Real-time state estimators for robotics, automation, and embedded systems
Sensor fusion pipelines combining IMU, encoder, and visual data
Kalman filters in Python (Jupyter) and C++ (Arduino)
A working motion model for a robot joint with live telemetry
🧠 What You’ll Learn
Why classic filters fail—and how Kalman succeeds in dynamic, uncertain systems
How to tune and optimize Kalman gains for precision and convergence
What “covariance” really means—and how to use it without fear
How to model motion systems (like DC motors) with matrices and variance
When and why to fuse multiple sensors—and how to do it in one line of code
🎓 Who This Is For
You’ve hit the wall with “plug and play” estimation. This course is for you if:
You’re building robots, drones, or autonomous systems
You’ve written Kalman code—but didn’t trust it
You want a mental model of prediction-correction, not just an API call
You’re moving from web/dev to embedded/robotics
You’re teaching yourself, and want your intuition to match your math
💻 Prerequisites
Comfort with basic Python, algebra, and linear equations
No formal control theory required—we’ll build your model step by step
Ideal for self-taught engineers, robot tinkerers, or cross-discipline learners
🧰 Tools & Workflow
Python (Jupyter + Octave/Matlab) for rapid prototyping
C++ with CMake and Arduino IDE for microcontroller deployment
GitHub repos provided for all code examples
Simulated + hardware-based use cases included