<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Kalman Smoothing | A Quest After Perspectives</title><link>https://iphysresearch.github.io/blog/tag/kalman-smoothing/</link><atom:link href="https://iphysresearch.github.io/blog/tag/kalman-smoothing/index.xml" rel="self" type="application/rss+xml"/><description>Kalman Smoothing</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Thu, 03 Jul 2025 00:00:00 +0000</lastBuildDate><image><url>https://iphysresearch.github.io/blog/media/sharing.png</url><title>Kalman Smoothing</title><link>https://iphysresearch.github.io/blog/tag/kalman-smoothing/</link></image><item><title>A Kalman-smoother based data imputation strategy to data gaps in spaceborne gravitational wave detectors</title><link>https://iphysresearch.github.io/blog/mypublication/2025_kalman_gap_imputation/</link><pubDate>Thu, 03 Jul 2025 00:00:00 +0000</pubDate><guid>https://iphysresearch.github.io/blog/mypublication/2025_kalman_gap_imputation/</guid><description>&lt;p>The proposed imputation strategy addresses gaps in long-duration observations. Comparisons with windowing and data-augmentation approaches examine the tradeoff between inference bias and computational effort.&lt;/p>
&lt;h2 id="sources">Sources&lt;/h2>
&lt;ul>
&lt;li>&lt;a href="https://arxiv.org/abs/2507.02458" target="_blank" rel="noopener">arXiv abstract and submission history&lt;/a>&lt;/li>
&lt;/ul></description></item></channel></rss>