Kalman Filter Courses and Certifications

State Estimation and Localization for Self-Driving Cars

842 ratings at Coursera

Estimate a self-driving car's pose from noisy sensors: apply least squares, extended and unscented Kalman filters, GNSS/IMU models, and LIDAR ICP scan matching in CARLA.

• Coursera
• 6 weeks, 4 hours a week
• Self-Paced
• Paid Course

卡尔曼滤波与组合导航

以卡尔曼滤波为核心，学习惯性、卫星与天文组合导航的估计理论、建模与应用。

• XuetangX
• Self-Paced
• Free Online Course

University of Colorado System;  CU Boulder

Battery State-of-Charge (SOC) Estimation

254 ratings at Coursera

Implement battery state-of-charge estimators in Octave/MATLAB, from simple voltage- and current-based methods to linear, extended, and sigma-point Kalman filters, plus bar-delta efficiency.

• Coursera
• 7 weeks, 4 hours a week
• Self-Paced
• Paid Course

Campus - the Israeli National Project for Digital Learning

Bayesian Algorithms for Self-Driving Cars

1 rating

"Bayesian Algorithms for Self-Driving Cars" is an advanced Computer science course, designed to equip the student with the most important localization algorithms now deployed in modern autonomous vehi

• edX
• 13 weeks, 2-3 hours a week
• Self-Paced
• Free Certificate

Sensor Fusion and Non-linear Filtering for Automotive Systems

7 ratings at edX

Learn fundamental algorithms for sensor fusion and non-linear filtering with application to automotive perception systems.

• edX
• 9 weeks, 10-20 hours a week
• Self-Paced
• Free Online Course (Audit)

Career Certificate

Sensor Fusion and Multi-Object Tracking

Learn how to accurately position objects in traffic and how to track them, for self-driving vehicle applications.

• edX
• 19 weeks, 10-20 hours a week
• Self-Paced
• $493.00
• 2 courses

Perception Algorithms for Autonomous Systems

Learn how autonomous platforms perceive environments by combining radar, cameras, lidar, and other sensors for tracking, fusion, and target classification.

• Swayam
• Available until Oct 9, 2026
• Free Online Course

NPTEL;  IIT Kharagpur

Estimation of Signals and Systems

This course covers lessons on probability theory, random variables, mean and variance, linear signal models, z-transform, Kalman filter, variants of least squares estimation, and estimation problems in instrumentation and control.

• YouTube
• 1 day 2 hours
• Self-Paced
• Free Video

NPTEL;  IIT Madras

Introduction to Robotics

This course is a bridge course for students from various disciplines to get the basic understanding of robotics. The mechanical, electrical, and computer science aspects of robotics is covered in this introductory course.

• YouTube
• 1 day 4 hours
• Self-Paced
• Free Video

Control Bootcamp

2 ratings

A rapid, example-driven overview of optimal and robust control, from controllability and Kalman filtering to LQR and feedback design in MATLAB.

• YouTube
• 11 hours
• Self-Paced
• Free Video

Strange Loop Conference

Crowd Dynamics, RL and Unity - A Journey

Explores reinforcement learning for crowd dynamics, combining PPO in Unity-ML with extended Kalman filters to model varied environments.

• YouTube
• 35 minutes
• Self-Paced
• Conference Talk

JuliaHub

System Identification with Julia: Sensor Fusion - Part 13

Master sensor fusion techniques for pendulum systems by combining acceleration and gyroscope data using ModelingToolkit and Julia to create unbiased state estimates through multi-rate sensor integration.

• YouTube
• 30 minutes
• Self-Paced
• Free Video

JuliaHub

LQR and LQG Design in Control Analysis Using Julia - Part 7

Master LQR and LQG controller design in Julia, covering simulation, Kalman filters, closed-loop analysis, and output penalties using ControlSystems.jl and RobustAndOptimalControl.jl packages.

• YouTube
• 22 minutes
• Self-Paced
• Free Video

JuliaHub

Control of Rotary Pendulum - Parameter Estimation Using Maximum-Likelihood - Part 5

Dive into maximum-likelihood estimation for rotary pendulum control using unscented Kalman filters and gradient-based optimization with automatic differentiation in Julia.

• YouTube
• 15 minutes
• Self-Paced
• Free Video

The Julia Programming Language

StateSpaceDynamics.jl - Probabilistic State-Space Modeling

Discover StateSpaceDynamics.jl for probabilistic state-space modeling in Julia, featuring non-Gaussian observations, hierarchical models, and efficient algorithms for neuroscience data.

• YouTube
• 12 minutes
• Self-Paced
• Free Video