diff --git a/selfdrive/locationd/lagd.py b/selfdrive/locationd/lagd.py
index c42d2738ce..8a47f99a98 100755
--- a/selfdrive/locationd/lagd.py
+++ b/selfdrive/locationd/lagd.py
@@ -1,8 +1,7 @@
 #!/usr/bin/env python3
 import numpy as np
 from collections import deque
-
-from skimage.registration._masked_phase_cross_correlation import cross_correlate_masked
+from functools import partial
 
 import cereal.messaging as messaging
 from cereal import car, log
@@ -155,22 +154,68 @@ class LagEstimator:
   def correlation_lags(self, sig_len, dt):
     return np.arange(0, sig_len) * dt
 
-  def actuator_delay(self, expected_sig, actual_sig, is_okay, dt, max_lag=1.):
-    # masked (gated) normalized cross-correlation
-    # normalized, can be used for anything, like comparsion
+  def actuator_delay(self, expected_sig, actual_sig, mask, dt, max_lag=1.):
+    """
+    References:
+      D. Padfield. "Masked FFT registration". In Proc. Computer Vision and
+      Pattern Recognition, pp. 2918-2925 (2010).
+      :DOI:`10.1109/CVPR.2010.5540032`
+    """
+
+    eps = np.finfo(np.float64).eps
+    expected_sig = np.asarray(expected_sig, dtype=np.float64)
+    actual_sig = np.asarray(actual_sig, dtype=np.float64)
+
+    expected_sig[~mask] = 0.0
+    actual_sig[~mask] = 0.0
+
+    rotated_expected_sig = expected_sig[::-1]
+    rotated_mask = mask[::-1]
+
+    n = len(expected_sig) + len(actual_sig) - 1
+    fft = partial(np.fft.fft, n=n)
+
+    actual_sig_fft = fft(actual_sig)
+    rotated_expected_sig_fft = fft(rotated_expected_sig)
+    actual_mask_fft = fft(mask.astype(np.float64))
+    rotated_mask_fft = fft(rotated_mask.astype(np.float64))
+
+    number_overlap_masked_samples = np.fft.ifft(rotated_mask_fft * actual_mask_fft).real
+    number_overlap_masked_samples[:] = np.round(number_overlap_masked_samples)
+    number_overlap_masked_samples[:] = np.fmax(number_overlap_masked_samples, eps)
+    masked_correlated_actual_fft = np.fft.ifft(rotated_mask_fft * actual_sig_fft).real
+    masked_correlated_expected_fft = np.fft.ifft(actual_mask_fft * rotated_expected_sig_fft).real
+
+    numerator = np.fft.ifft(rotated_expected_sig_fft * actual_sig_fft).real
+    numerator -= masked_correlated_actual_fft * masked_correlated_expected_fft / number_overlap_masked_samples
+
+    actual_squared_fft = fft(actual_sig ** 2)
+    actual_sig_denom = np.fft.ifft(rotated_mask_fft * actual_squared_fft).real
+    actual_sig_denom -= masked_correlated_actual_fft ** 2 / number_overlap_masked_samples
+    actual_sig_denom[:] = np.fmax(actual_sig_denom, 0.0)
+
+    rotated_expected_squared_fft = fft(rotated_expected_sig ** 2)
+    expected_sig_denom = np.fft.ifft(actual_mask_fft * rotated_expected_squared_fft).real
+    expected_sig_denom -= masked_correlated_expected_fft ** 2 / number_overlap_masked_samples
+    expected_sig_denom[:] = np.fmax(expected_sig_denom, 0.0)
+
+    denom = np.sqrt(actual_sig_denom * expected_sig_denom)
 
-    assert len(expected_sig) == len(actual_sig)
+    # zero-out samples with very small denominators
+    tol = 1e3 * eps * np.max(np.abs(denom), keepdims=True)
+    nonzero_indices = denom > tol
 
-    xcorr = cross_correlate_masked(actual_sig, expected_sig, is_okay, is_okay, axes=tuple(range(actual_sig.ndim)),)
-    lags = self.correlation_lags(len(expected_sig), dt)
+    ncc = np.zeros_like(denom, dtype=np.float64)
+    ncc[nonzero_indices] = numerator[nonzero_indices] / denom[nonzero_indices]
+    np.clip(ncc, -1, 1, out=ncc)
 
-    n_frames_max_delay = int(max_lag / dt)
-    xcorr = xcorr[len(expected_sig) - 1: len(expected_sig) - 1 + n_frames_max_delay]
-    lags = lags[:n_frames_max_delay]
+    max_lag_samples = int(max_lag / dt)
+    roi_ncc = ncc[len(expected_sig) - 1: len(expected_sig) - 1 + max_lag_samples]
 
-    max_corr_index = np.argmax(xcorr)
+    max_corr_index = np.argmax(roi_ncc)
+    corr = roi_ncc[max_corr_index]
+    lag = max_corr_index * dt
 
-    lag, corr = lags[max_corr_index], xcorr[max_corr_index]
     return lag, corr