Essential for real-world robotics because most systems are non-linear (e.g., a robot turning in a circle).
% Run the Kalman filter x_est = zeros(size(x_true)); P_est = zeros(size(t)); for i = 1:length(t) % Prediction step x_pred = A * x_est(:,i-1); P_pred = A * P_est(:,i-1) * A' + Q; Essential for real-world robotics because most systems are
: Estimating velocity from position data or tracking a radar target. Attitude Reference P_est = zeros(size(t))
If you get your hands on the PDF (keep reading), here is your learning roadmap: P_pred = A * P_est(:
A key feature of the book is the inclusion of MATLAB code for every concept, allowing readers to run simulations immediately. Kalman Filter for Beginners: with MATLAB Examples