<html><head></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><div>Thanks a lot to all of you for your hints and pointers to such interesting information!</div><div><br></div><div>In fact for practical purposes, I will experiment with GPS first and see how this works (from a first impression, the acceleration data from the phone seem extremely "hyper-sensitive", so it might anyway turn out to be impossible calibrating them...)</div><div><br></div><div>On the other hand,  the Kalman filter looks very interesting (and the whole topic of estimation/prediction is, of course) , and it's something I like especially about this list that you so often get pointed to fascinating things (in fact, Racket & functional programming being "just a hobby"  for me, perhaps the main reason I follow this list - and try to find a bit of spare time for Racket programming myself - is that it's so intellectually stimulating and pointing to all kinds of topics (mathematical etc.) I've been ignorant of for too long :-;</div><div><br></div><div>Sigrid </div><div><br></div><div><br></div><div><br></div><div>On Tue, Nov 30, 2010 at 4:02 PM, Will M. Farr <<a href="mailto:wmfarr@gmail.com">wmfarr@gmail.com</a>> wrote:<br><blockquote type="cite">In particular, combining noisy measurements in the context of an ODE that describes the evolution of a system (in this case, you measure a = dv/dt = d^x/dt^2, and want to "integrate" to find x(t)) is often done using a Kalman filter:<br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite"><a href="http://en.wikipedia.org/wiki/Kalman_filter">http://en.wikipedia.org/wiki/Kalman_filter</a><br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite">This is also almost certainly the approach you would take if you want to combine data from a GPS unit with the accelerometer data. ?Kalman filters are often used in commercial inertial navigation systems (i.e. in planes) to track position as well. ?The literature on the subject is *very* extensive, if you enjoy that sort of reading. ?Alternately, from the basic description it can be fun to work out a lot of the simple results yourself (depending, of course, on how much you enjoy math and what your level of experience with statistics and differential equations are). ?In practice (from someone who is not in the field of inertial navigation, but has heard talks about it) it seems like the "tuning" of the filter is as much art as science, so I wouldn't necessarily assign too much weight to the prior literature in your case.<br></blockquote><br><br>The black art of tuning applies mostly to PIDs as mentioned in another<br>thread, and should be a part of every PhD :P<br><br>But for a good introduction to applied Kalman filtering, check out<br>Probabilistic Robotics from the library and it will show you how to<br>proceed.<br><br>I will however take one last opportunity to repeat my original advice<br>of just using GPS for estimating a person's velocity. Frankly, the<br>gain in accuracy from integrating data from a cheap accelerometer into<br>a Kalman filter with GPS data is often not worth it. Only do so after<br>the GPS has proven too inaccurate.<br><br>Anthony<br><br></div></body></html>