Before jumping into the full Kalman equations, it's essential to understand recursive expressions. A recursive filter uses the previous estimate and a new measurement to calculate the current estimate, rather than storing a massive history of data.
: Introduction to exponential moving averages and filtering high-frequency noise. dandelon.com Part II: The Kalman Filter Theory The Algorithm : Presented as a two-step "Prediction" and "Update" loop. Prediction : Projects the current state forward in time. Before jumping into the full Kalman equations, it's
: Estimating velocity from noisy position data (e.g., sonar or GPS). Radar Tracking dandelon
where H is the measurement matrix, and v is a measurement noise. Radar Tracking where H is the measurement matrix,
% Initialize x = 0; % Initial state P = 1; % Initial uncertainty Q = 0.1; % Process noise R = 0.5; % Measurement noise measurements = randn(1,100); % Noisy data
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