Kalman Filter

Kalman filtering (and filtering in general) considers the following setting: we have a sequence of states  x_t, which evolves under random perturbations over time. Unfortunately we cannot observe x_t, we can only observe some noisy function of  x_t, namely,  y_t. Our task is to find the best estimate of x_t given our observations of y_t. Continue reading “Kalman Filter”