The filter receives new, noisy data from a sensor and "corrects" its prediction.
for k = 1:n_iter
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: Forecasts the future state based on past data and a physical model. Update (Correction) Step
It only needs the previous state to calculate the current state. You don't need a massive database of past readings.
The Kalman filter is a recursive algorithm that uses a combination of prediction and measurement updates to estimate the state of a system. It takes into account the uncertainty of the measurements and the system dynamics to produce an optimal estimate of the state.
%% Kalman Filter Example 2: Falling Object with Gravity clear; clc; close all;