diff --git a/rednose/helpers/ekf_sym.py b/rednose/helpers/ekf_sym.py
index 9dffe50..2a0b416 100644
--- a/rednose/helpers/ekf_sym.py
+++ b/rednose/helpers/ekf_sym.py
@@ -391,7 +391,7 @@ def augment(self):
     assert self.P.shape == (self.dim_err, self.dim_err)
 
   def state(self):
-    return np.array(self.x).flatten()
+    return np.array(self.x).ravel()
 
   def covs(self):
     return self.P
@@ -461,27 +461,26 @@ def predict(self, t):
     self.normalize_quaternions()
     self.filter_time = t
 
-  def predict_and_update_batch(self, t, kind, z, R, extra_args=[[]], augment=False):  # pylint: disable=dangerous-default-value
+  def predict_and_update_batch(self, t, kind, z, R, xk_km1=None, Pk_km1=None, xk_k=None, Pk_k=None, y=None, extra_args=[[]], augment=False):  # pylint: disable=dangerous-default-value
     # TODO handle rewinding at this level"
 
     # rewind
     if self.filter_time is not None and t < self.filter_time:
       if len(self.rewind_t) == 0 or t < self.rewind_t[0] or t < self.rewind_t[-1] - self.max_rewind_age:
         self.logger.error(f"observation too old at {t:.3f} with filter at {self.filter_time:.3f}, ignoring")
-        return None
+        return False
       rewound = self.rewind(t)
     else:
       rewound = []
 
-    ret = self._predict_and_update_batch(t, kind, z, R, extra_args, augment)
+    self._predict_and_update_batch(t, kind, z, R, extra_args, xk_km1, Pk_km1, xk_k, Pk_k, y, augment)
 
     # optional fast forward
     for r in rewound:
       self._predict_and_update_batch(*r)
+    return True
 
-    return ret
-
-  def _predict_and_update_batch(self, t, kind, z, R, extra_args, augment=False):
+  def _predict_and_update_batch(self, t, kind, z, R, extra_args, xk_km1=None, Pk_km1=None, xk_k=None, Pk_k=None, y=None, augment=False):
     """The main kalman filter function
     Predicts the state and then updates a batch of observations
     dim_x: dimensionality of the state space
@@ -497,6 +496,8 @@ def _predict_and_update_batch(self, t, kind, z, R, extra_args, augment=False):
     assert z.shape[0] == R.shape[0]
     assert z.shape[1] == R.shape[1]
     assert z.shape[1] == R.shape[2]
+    assert y is None or z.shape == y.shape
+    # assert stuff
 
     # initialize time
     if self.filter_time is None:
@@ -507,10 +508,13 @@ def _predict_and_update_batch(self, t, kind, z, R, extra_args, augment=False):
     assert dt >= 0
     self.x, self.P = self._predict(self.x, self.P, dt)
     self.filter_time = t
-    xk_km1, Pk_km1 = np.copy(self.x).flatten(), np.copy(self.P)
+
+    if xk_km1 is not None:
+      np.copyto(xk_km1, self.x.ravel())
+    if Pk_km1 is not None:
+      np.copyto(Pk_km1, self.P)
 
     # update batch
-    y = []
     for i in range(len(z)):
       # these are from the user, so we canonicalize them
       z_i = np.array(z[i], dtype=np.float64, order='F')
@@ -519,8 +523,13 @@ def _predict_and_update_batch(self, t, kind, z, R, extra_args, augment=False):
       # update
       self.x, self.P, y_i = self._update(self.x, self.P, kind, z_i, R_i, extra_args=extra_args_i)
       self.normalize_quaternions()
-      y.append(y_i)
-    xk_k, Pk_k = np.copy(self.x).flatten(), np.copy(self.P)
+      if y is not None:
+        np.copyto(y_i, y[i])
+
+    if xk_k is not None:
+      np.copyto(xk_k, self.x.ravel())
+    if Pk_k is not None:
+      np.copyto(Pk_k, self.P)
 
     if augment:
       self.augment()
@@ -528,8 +537,6 @@ def _predict_and_update_batch(self, t, kind, z, R, extra_args, augment=False):
     # checkpoint
     self.checkpoint((t, kind, z, R, extra_args))
 
-    return xk_km1, xk_k, Pk_km1, Pk_k, t, kind, y, z, extra_args
-
   def _predict_python(self, x, P, dt):
     x_new = np.zeros(x.shape, dtype=np.float64)
     self.f(x, dt, x_new)