Package pylearn :: Package algorithms :: Package sandbox :: Module kalman
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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).

Classes [hide private]
KalmanModule