Kalman Filter for Beginners, Part 1 - Recursive Filters & MATLAB Examples

You can use the Kalman Filter—even without mastering all the theory. In Part 1 of this three-part beginner series, I break it down step by step, starting with simple recursive filters: average, moving average, and low-pass filters. You’ll see how these relate to the Kalman Filter--and watch MATLAB demos bring them to life (Python provided below as well). No jargon, just clear explanations to build your estimation and data analysis skills. 🎥 *Watch the Full Kalman Filter Series:* http://tinyurl.com/kalmanfilters 👩🏽‍💻 *This video covers:* • What is a recursive filter? • How do moving average and low-pass filters work? • MATLAB examples for noisy data • Foundations of the Kalman Filter algorithm 🛠️ *Resources:* • MATLAB code (free): https://tinyurl.com/kalmanfilterforbeginners • Lecture notes (PDF): https://tinyurl.com/kalmanfilterforbeginners • Python implementation by Adam Steineck: https://gitlab.com/adam-at-epsilon/digital-filters • Book reference: Kalman Filter for Beginners by Phil Kim → https://www.amazon.com/dp/1463648359 ⚠️ *Corrections:* • At 10:58, I say the noise was uniformly distributed. It was actually normally distributed (std. dev. = 4). • At 14:10, I clarify that randn in MATLAB gives a normal distribution. 📺 *Related Videos:* ▶️ Part 2: Estimation & Prediction in Kalman Filters → https://youtu.be/qCZ2UTgLM_g ⏮️ Previous: Attitude Determination – Davenport’s Q-Method → https://youtu.be/W1k1LEHLXTM This special lecture series takes us into *dynamic* attitude estimation, using time-varying gyroscope data, as opposed to the previously covered *static* attitude estimation, which uses simultaneous measurements of known external objects. ⏱️ *Chapters* 0:00 Introduction 0:21 Recursive expression for average 5:52 Simple example of recursive average filter 10:21 MATLAB demo of recursive average filter for noisy data 17:55 Moving average filter 21:14 MATLAB moving average filter example 26:49 Low-pass filter 37:03 MATLAB low-pass filter example 41:03 Basics of the Kalman Filter algorithm 👨‍🏫 *About Me:* I’m Dr. Shane Ross, professor of aerospace engineering at Virginia Tech (PhD from Caltech, former NASA/JPL & Boeing). I teach and research nonlinear dynamics, estimation, space navigation, and chaotic transport. More at shaneross.com 🔔 *Subscribe for more lectures:* https://is.gd/RossLabSubscribe 𝕏 *Follow:* https://x.com/RossDynamicsLab ► *Space Vehicle Dynamics course videos (playlist)* https://is.gd/SpaceVehicleDynamics ► *Video Courses & Playlists by Professor Ross* ▶️ Kalman Filters for Beginners: http://tinyurl.com/kalmanfilters ▶️ Nonlinear Dynamics & Chaos https://is.gd/NonlinearDynamics ▶️ Hamiltonian Dynamics https://is.gd/AdvancedDynamics ▶️ 3-Body Problem Orbital Dynamics https://is.gd/3BodyProblem ▶️ Center Manifolds, Normal Forms, & Bifurcations https://is.gd/CenterManifolds ▶️ Space Vehicle Dynamics https://is.gd/SpaceVehicleDynamics ▶️ Lagrangian & 3D Rigid Body Dynamics https://is.gd/AnalyticalDynamics ▶️ Space Manifolds https://is.gd/SpaceManifolds Keywords & Topics: Kalman filter, estimation, MATLAB, recursive filter, moving average, low-pass filter, dynamic systems, orbital mechanics, space dynamics, sensor fusion, attitude estimation, CR3BP, nonlinear dynamics, celestial mechanics, Lyapunov orbits, Lagrange points, interplanetary highways, space manifolds, cislunar space, Virginia Tech, Caltech, JPL, NASA, aerospace #kalmanfilter #MATLAB #lowpass #python #mathematics #recursion #nonlineardynamics #CR3BP #spacemanifolds #estimation #dynamics #cislunar #aerospace #chaos #dynamicalsystems #spaceengineering