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<div class="moz-cite-prefix">I've updated my 1D linear Kalman filter
to include an analysis method to improve the initial noise model,
along with a proper help file.<br>
<br>
<a href="https://github.com/jwmatthys/kalman-pd">https://github.com/jwmatthys/kalman-pd</a><br>
<br>
Joel<br>
<br>
On 02/28/2013 05:05 PM, Charles Z Henry wrote:<br>
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cite="mid:CAPfmNOGZuJHwRU43VEcge38mOksGZiRdVe5j5WJUpbWFrgWTKA@mail.gmail.com"
type="cite">
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<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>
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Chuck<br>
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<div><br>
<br>
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<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 moz-do-not-send="true"
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 moz-do-not-send="true"
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>
<br>
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