<p>Here are three tests mentioned in <a href="https://www.robots.ox.ac.uk/%7Eian/Teaching/Estimation/LectureNotes2.pdf" rel="nofollow noreferrer">[1]</a>:</p>
<ol>
<li><strong>Innovation magnitude bound test:</strong> basicaly Comparing innovations against sigma points obtained from matrix S.</li>
<li><strong>Chi-squared test on nomalized innovations squared:</strong> The normalied innovation squared is supposed to have a chi-squared distribution.</li>
<li><strong>Innovation whiteness (autocorrelation) test:</strong> Innovations are supposed to be white, hence the test on whiteness.</li>
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<p>All the three are well explained in <a href="https://www.robots.ox.ac.uk/%7Eian/Teaching/Estimation/LectureNotes2.pdf" rel="nofollow noreferrer">[1]</a>. Another useful reference for performing the second test is <a href="https://arxiv.org/pdf/1807.08855.pdf" rel="nofollow noreferrer">[2]</a>.</p>
In Extended Kalman Filter how to test residual (innovation) any idea
<p>Statistical test is required in terms of residual check.</p>

<p>How to test residual in matlab? I was plotting it.</p>

<ol>
<li>I found that innovation term is oscillating considerably around zero in a zig zag manner.</li>
<li>Is innovation expected to zero? If yes how to do that?</li>
</ol>