class_docstr: Class for a general Kalman filter working on Lie groups.
  
  Given an adapted model, it provides the tools to carry out non-linear state
  estimation with an error modeled on the Lie algebra. The model must provide
  the functions to propagate and update a state, the observation model, and
  the computation of the Jacobians.
  
  Parameter
  ---------
  model : {class, instance}
      Object representing an observed dynamical system.
code: def __init__(self, model):
      self.model = model
      self.state = model.group.identity
      self.covariance = gs.zeros(
          (self.model.group.dim, self.model.group.dim)
      )
      self.process_noise = gs.zeros(
          (self.model.dim_noise, self.model.dim_noise)
      )
      self.measurement_noise = gs.zeros(
          (self.model.dim_obs, self.model.dim_obs)
      )
  
imports: import geomstats.backend
  from geomstats.geometry.euclidean import Euclidean
  from geomstats.geometry.matrices import Matrices
  from geomstats.geometry.special_euclidean import SpecialEuclidean
label_desc: Use image node_id 1 to create a new KalmanFilter object  with example: obj = KalmanFilter(model)