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gerdm committed Mar 16, 2024
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Expand Up @@ -77,7 +77,7 @@ <h2 property="alternativeHeadline">Outlier-robust Kalman Filtering through gener


<figure>
<img src="./intro-plot.png" style="width: 80%; height:auto">
<img src="intro-plot.png" style="width: 80%; height:auto">
<figcaption>
The dotted blue line shows the KF posterior mean estimate and
the solid orange line shows the WoLF posterior mean estimate.
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</p>

<p>
<strong>Tresholded Mahalanobis-based weighting function (TMD) </strong>
<strong>Thresholded Mahalanobis-based weighting function (TMD) </strong>
$$
W_{t}(\bm y_{1:t}) =
\begin{cases}
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<ol>
<strong>Remarks</strong>
<li> The Kalman filter is <strong>not</strong> outlier-robust.</li>
<li> The IMQ and TMD are outlier-robust.</li>
<li> Filters with IMQ and TMD weighting function are outlier-robust.</li>
</ol>

<figure>
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</tr>
<tr>
<td>KF-B</td>
<td>\(O(I_{-\epsilon}\,p^3)\)</td>
<td>\(O(I\,p^3)\)</td>
<td>3</td>
<td>Wang2018</td>
</tr>
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</tr>
</tbody>
<caption>
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.
</caption>
</table>
</article>
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<footer>
<aside>
<pre>github.com/gerdm/weighted-likelihood-filter</pre>
<small>
<pre>github.com/gerdm/weighted-likelihood-filter</pre>
</small>
</aside>
<div>
</div>
<aside>
</aside>
</footer>
</body>
</html>
</html>

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