deepof.utils.rts_smoother_numba

deepof.utils.rts_smoother_numba(measurements, F, H, Q, R)

Implements the Rauch-Tung-Striebel (RTS) smoother for state estimation.

This function performs both forward and backward passes to estimate the optimal state sequence given a set of noisy measurements. It first applies the Kalman filter in a forward pass and then refines the estimates using the RTS smoother in a backward pass.

Parameters:

• measurements (np.ndarray) – Array of measurements, shape (n_timesteps, n_dim_measurement).

• F (np.ndarray) – State transition matrix, shape (n_dim_state, n_dim_state).

• H (np.ndarray) – Observation matrix, shape (n_dim_measurement, n_dim_state).

• Q (np.ndarray) – Process noise covariance matrix, shape (n_dim_state, n_dim_state).

• R (np.ndarray) – Measurement noise covariance matrix, shape (n_dim_measurement, n_dim_measurement).

Returns:

Smoothed state estimates, shape (n_timesteps, n_dim_state).

Return type:

smoothed_states (np.ndarray)