<div dir="ltr"><div><div><div>Hey Joel<br><br></div>I was very interested to see your implementation. It's drastically simpler than I thought it would be. Well, you did mention it was simple :) However, I thought the math was pretty expensive to do and complex to program.<br>
<br></div>I like the approach generally--you have parameters for the assumed noise model and methods to set them (better than trying to build a monolith that does both the measurement and filtering). Do you have another patch or abstraction to analyze the sensor data and calculate those parameters? If so, you should add it to git.<br>
<br></div>Chuck<br><div><div><br><br></div></div></div><div class="gmail_extra"><br><br><div class="gmail_quote">On Thu, Feb 28, 2013 at 12:47 PM, Joel Matthys <span dir="ltr"><<a href="mailto:jwmatthys@gmail.com" target="_blank">jwmatthys@gmail.com</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
<div text="#000000" bgcolor="#FFFFFF">
<font face="Ubuntu">I just completed a very simple 1D Kalman filter
Pd external. I haven't really done any documentation on it, but it
seems pretty robust for cleaning up 1D sensor inputs.<br>
<br>
The source is here:<br>
</font><br>
<a href="https://github.com/jwmatthys/kalman-pd" target="_blank">https://github.com/jwmatthys/kalman-pd</a><span class="HOEnZb"><font color="#888888"><br>
<br>
<font face="Ubuntu">Joel</font><br>
</font></span></div>
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