# In terminal, navigate to your folder zip -r Kalman_Beginner_Package.zip kalman_beginner_example1.m kalman_beginner_example2.m README.txt
%% 3. KALMAN FILTER LOOP for k = 1:N % --- PREDICTION STEP --- x_pred = F * x_est; % Predict state P_pred = F * P_est * F' + Q; % Predict covariance # In terminal, navigate to your folder zip
In this article, we will break down the Kalman Filter into simple, digestible pieces and—most importantly—provide you with Part 1: The Core Intuition (Without the Math, Yet) Before we dive into matrices and equations, let's understand the logic with a simple story. # In terminal
% --- CORRECTION STEP (Using the measurement) --- z = measurements(k); % Current measurement y = z - H * x_pred; % Innovation (measurement residual) S = H * P_pred * H' + R; % Innovation covariance K = P_pred * H' / S; % Kalman Gain % Predict covariance In this article
% Storage for results stored_x = zeros(2, N); stored_P = zeros(2, 2, N);