@@ -180,8 +180,8 @@ called in several forms:
   * ``L, P, E = lqe(A, G, C, QN, RN)``
   * ``L, P, E = lqe(A, G, C, QN, RN, NN)``
 
-where :code:`sys` is an :class:`LTI` object, and `A`, `G`, `C`, `QN`, `RN`,
-and `NN` are 2D arrays of appropriate dimension.
+where :code:`sys` is a :class:`StateSpace` object, and `A`, `G`, `C`,
+`QN`, `RN`, and `NN` are 2D arrays of appropriate dimension.
 
 For the state-space system form ``lqe(sys, QN, RN)``, the process noise
 matrix :math:`G` is taken to be :code:`sys.B`.  This models disturbances
@@ -193,11 +193,10 @@ systems.  To model noise entering through a different matrix, specify
 For example, use ``G = np.eye(sys.nstates)`` if independent disturbances
 are modelled as entering every state derivative.
 
-If :code:`sys` is a
-discrete-time system, the first two forms will compute the discrete
-time optimal controller.  For the second two forms, the :func:`dlqr`
-function can be used.  Additional arguments and details are given on
-the :func:`lqr` and :func:`dlqr` documentation pages.
+If :code:`sys` is a discrete-time system, the first two forms call
+:func:`dlqe` to compute the discrete-time estimator.  For the matrix
+forms, call :func:`dlqe` directly.  Additional arguments and details
+are given on the :func:`lqe` and :func:`dlqe` documentation pages.
 
 .. testsetup:: kalman