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.
docstr: Propagate the estimate and its covariance.
  
  Given the propagation Jacobian F and the noise Jacobian G, the
  covariance P becomes F P F^T + G Q G^T.
  
  Parameters
  ----------
  sensor_input : array-like
      Vector representing the propagation sensor input.
docstr_tok: ["Propagate","the","estimate","and","its","covariance",".","Given","the","propagation","Jacobian","F","and","the","noise","Jacobian","G",",","the","covariance","P","becomes","F","P","F^T","+","G","Q","G^T",".","Parameters","--","--","--","--","--","sensor_input",":","array-like","Vector","representing","the","propagation","sensor","input","."]
code: def propagate(self, sensor_input):
      """Propagate the estimate and its covariance.
  
      Given the propagation Jacobian F and the noise Jacobian G, the
      covariance P becomes F P F^T + G Q G^T.
  
      Parameters
      ----------
      sensor_input : array-like
          Vector representing the propagation sensor input.
      """
      prop_noise = self.process_noise
      prop_jac = self.model.propagation_jacobian(
          self.state, sensor_input
      )
      noise_jac = self.model.noise_jacobian(self.state, sensor_input)
  
      prop_cov = Matrices.mul(
          prop_jac, self.covariance, Matrices.transpose(prop_jac)
      )
      noise_cov = Matrices.mul(
          noise_jac, prop_noise, Matrices.transpose(noise_jac)
      )
      self.covariance = prop_cov + noise_cov
      self.state = self.model.propagate(self.state, sensor_input)
  
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 3 for calling the KalmanFilter obj's underlying member method code with example usage: obj.propagate(sensor_input) without return types