# Module kalman

source code

Modules and misc. code related to the Kalman Filter.
Kalman filter algorithm as presented in "Probabilistic Robotics"
x_t is the state
u_t is a control vector
z_t is the observation vector
\epsilon_t is a random noise term with zero mean and covariance R_t.
\delta_t is a random noise term with zero mean and covariance Q_t.
state (x_t) evolves according to
x_t = A_t x_{t-1} + B_t u_t + \epsilon_t
Observation z_t is made according to
z_t = C_t x_t + \delta_t
Assume that the distribution over initial states is a Gaussian.
With these linear/Gaussian assumptions, the belief about the state all times t is Gaussian, so
we can represent it compactly by the mean (mu) and the covariance (sigma).