Spend time understanding Q and R. They are the heart of your filter.
The filter starts with an initial guess (0 m position, 10 m/s velocity). As each noisy GPS reading arrives, the Kalman filter computes the optimal blend between the model prediction and the measurement. Notice how the position estimate (blue line) is much smoother than the noisy measurements (red dots), and the velocity converges to the true value (10 m/s).
% Tuning Q and R interactively clear; clc; --- Kalman Filter For Beginners With MATLAB Examples BEST
The filter assigns a Kalman Gain ($K$) to these two sources. If the measurement is very noisy (low trust), $K$ is small, and we rely more on the prediction. If the model is uncertain (high process noise), $K$ is large, and we rely more on the measurement.
%% 6. Visualization figure('Position', [100, 100, 800, 600]); Spend time understanding Q and R
% Storage for results est_pos = zeros(1, N); est_vel = zeros(1, N);
For beginners, the Kalman filter often looks like a wall of Greek letters and intimidating matrices. But here is the secret: It combines a noisy measurement with a rough prediction to produce a best guess . As each noisy GPS reading arrives, the Kalman
Before we write a single line of code, we need to solve the fundamental problem the Kalman Filter addresses:
% Process noise covariance Q (small for constant velocity model) Q = [0.01 0; 0 0.01];
