<?xml version="1.0" encoding="UTF-8"?>
<!-- generator="FeedCreator 1.8" -->
<?xml-stylesheet href="http://robosub-vm.eecs.wsu.edu/wiki/lib/exe/css.php?s=feed" type="text/css"?>
<rdf:RDF
    xmlns="http://purl.org/rss/1.0/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:slash="http://purl.org/rss/1.0/modules/slash/"
    xmlns:dc="http://purl.org/dc/elements/1.1/">
    <channel rdf:about="http://robosub-vm.eecs.wsu.edu/wiki/feed.php">
        <title>Palouse RoboSub Technical Documentation cs:localization:kalman:algorithm</title>
        <description></description>
        <link>http://robosub-vm.eecs.wsu.edu/wiki/</link>
        <image rdf:resource="http://robosub-vm.eecs.wsu.edu/wiki/lib/tpl/codowik/images/favicon.ico" />
       <dc:date>2026-04-03T13:44:33-0700</dc:date>
        <items>
            <rdf:Seq>
                <rdf:li rdf:resource="http://robosub-vm.eecs.wsu.edu/wiki/cs/localization/kalman/algorithm/start?rev=1485056035&amp;do=diff"/>
            </rdf:Seq>
        </items>
    </channel>
    <image rdf:about="http://robosub-vm.eecs.wsu.edu/wiki/lib/tpl/codowik/images/favicon.ico">
        <title>Palouse RoboSub Technical Documentation</title>
        <link>http://robosub-vm.eecs.wsu.edu/wiki/</link>
        <url>http://robosub-vm.eecs.wsu.edu/wiki/lib/tpl/codowik/images/favicon.ico</url>
    </image>
    <item rdf:about="http://robosub-vm.eecs.wsu.edu/wiki/cs/localization/kalman/algorithm/start?rev=1485056035&amp;do=diff">
        <dc:format>text/html</dc:format>
        <dc:date>2017-01-21T19:33:55-0700</dc:date>
        <dc:creator>Anonymous (anonymous@undisclosed.example.com)</dc:creator>
        <title>Kalman Filter Algorithm</title>
        <link>http://robosub-vm.eecs.wsu.edu/wiki/cs/localization/kalman/algorithm/start?rev=1485056035&amp;do=diff</link>
        <description>Kalman Filter Algorithm

Note: This section is currently under revision.

This section covers the Kalman Filter Algorithm. First we'll cover the State Space format of modeling and measuring a discrete-time dynamic system of estimated states, noisy inputs, and noisy measurements.  Second, we'll explore all the different pieces of information about our system necessary to inform the algorithm. Third, the specific Kalman Filter Algorithm constructed based off of those parameters. Finally, we'll use…</description>
    </item>
</rdf:RDF>
