( K_k = P_k-1 / (P_k-1 + R) ). Translation: How much do we trust the sensor vs. our model? If sensor noise ( R ) is small, gain ( K ) is high (trust sensor). If ( R ) is huge, gain ( K ) is low (ignore sensor).
The Kalman filter is a recursive algorithm that uses a combination of prediction and measurement updates to estimate the state of a system. It was first introduced by Rudolf Kalman in the 1960s and has since become a widely used algorithm in many fields. The Kalman filter is based on the following assumptions: kalman filter for beginners with matlab examples by phil kim
% Store estimates est_pos(k) = x(1); est_vel(k) = x(2); ( K_k = P_k-1 / (P_k-1 + R) )