EKF: Jacobian Matrix for the state estimate error covariance matrix (P) prediction

[afonsomm]

filterpy/filterpy/kalman/EKF.py

Lines 353 to 371 in 3b51149

	def predict(self, u=0):
	"""
	Predict next state (prior) using the Kalman filter state propagation
	equations.
	Parameters
	----------
	u : np.array
	Optional control vector. If non-zero, it is multiplied by B
	to create the control input into the system.
	"""
	self.predict_x(u)
	self.P = dot(self.F, self.P).dot(self.F.T) + self.Q
	# save prior
	self.x_prior = np.copy(self.x)
	self.P_prior = np.copy(self.P)

Shouldn't the Jacobian matrix of the system's dynamics be used in the prediction of (P) (line 367), instead of the transition matrix (F)?