.. _observer_module: ========================= Observer module ========================= The |project_name| Observer module contains several state (and parameter) observers. Observer are used to infer states and parameters from measurements. For a more detailed description of the methods refer to :ref:`the API `. The Observer module contains the following classes: - Moving Horizon Estimator (MHE) - Kalman Filter (KF) - Extended Kalman Filter (EKF) - Unscented Kalman Filter (UKF) - Particle Filter (PF) ----------------------------------- Moving Horizon Estimator ----------------------------------- Non-uniform sampling intervals ------------------------------- Multi-rate measurements ------------------------- ----------------------------------- Kalman Filter ----------------------------------- The class :class:`~hilo_mpc.KalmanFilter` (alias :class:`~hilo_mpc.KF`) implements the Kalman filter developed by Rudolf E. Kálmán. To set up the Kalman filter you need an already set up :class:`~hilo_mpc.Model` instance. Additionally you might want to supply a plot backend (via the :obj:`plot_backend` keyword argument) in order to visualize the estimation results later on. At the moment only `Matplotlib `_ and `Bokeh `_ are supported for plotting. The Kalman filter can be initialized as follows: .. code-block:: python from hilo_mpc import KF # Initialize Kalman filter kf = KF(model, plot_backend='bokeh') Required information, like e.g. the model dynamics or the sampling time, will be automatically extracted from the :class:`~hilo_mpc.Model` instance. ----------------------------------- Extended Kalman Filter ----------------------------------- ----------------------------------- Unscented Kalman Filter ----------------------------------- ----------------------------------- Particle Filter -----------------------------------