diff --git a/.gitignore b/.gitignore index edce96c..bed791d 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,5 @@ +.DS_Store + # Images *.pdf *.png diff --git a/docs/index.html b/docs/index.html index 8cef3f5..7a4f1e8 100644 --- a/docs/index.html +++ b/docs/index.html @@ -77,7 +77,7 @@

Outlier-robust Kalman Filtering through gener
- +
The dotted blue line shows the KF posterior mean estimate and the solid orange line shows the WoLF posterior mean estimate. @@ -154,7 +154,7 @@

Outlier-robust Kalman Filtering through gener
  1. The inverse multi-quadratic (IMQ) — a compensation-based weighting function, and -
  2. The tresholded Mahalanobis distance (TMD) — +
  3. The thresholded Mahalanobis distance (TMD) — a detect-and-reject weighting function.

@@ -167,7 +167,7 @@

Outlier-robust Kalman Filtering through gener

-Tresholded Mahalanobis-based weighting function (TMD) +Thresholded Mahalanobis-based weighting function (TMD) $$ W_{t}(\bm y_{1:t}) = \begin{cases} @@ -210,7 +210,7 @@

Outlier-robust Kalman Filtering through gener
    Remarks
  1. The Kalman filter is not outlier-robust.
  2. -
  3. The IMQ and TMD are outlier-robust.
  4. +
  5. Filters with IMQ and TMD weighting function are outlier-robust.
@@ -249,7 +249,7 @@

Outlier-robust Kalman Filtering through gener KF-B - \(O(I_{-\epsilon}\,p^3)\) + \(O(I\,p^3)\) 3 Wang2018 @@ -279,8 +279,9 @@

Outlier-robust Kalman Filtering through gener - Below, \(I\) is the number of inner iterations, \(I_{-\epsilon}\) is the number of inner iterations to reach a threshold \(\epsilon\), - \(p\) is the dimension of the state vector, and #HP is the number of hyperparameters. + Below, \(I\) is the number of inner iterations, + \(p\) is the dimension of the state vector, + and #HP is the number of hyperparameters. @@ -490,7 +491,9 @@

Data assimilation

- + \ No newline at end of file