That sounds great! Many applications need access to error statistics inside the GPS unit's filter. The DOP values are user-friendly aggregate measurements of this. Keep in mind though that really you are trusting the GPS unit's filter to return a useful DOP metric. This is probably wise as the GPS unit "knows more" (and the engineers who made the unit also probably know a lot!).<br><br>About the slower update rate of the DOP metrics. That just means you won't be able to update your filter gain as frequently. But your filter will still work. Sometimes, periodic recalculation of Kalman gain is done as a performance optimization as maintaining error statistics is somewhat expensive. I read about this in the context of contour tracking in a computer vision problem.<br><br>It may be that all filters are created roughly equal (do not know) or perhaps some manufacturers have some black arts of electronics and statistical filtering... The more I see while working with statistics -
the more I believe you have to be careful. There is no magic here. If you can, I advise remaining neutral about what to trust. Build your system so you have visibility into it, run experiments and analyze the data. Try to keep it simple. Statistics is different from debugging and troubleshooting in that it is often very hard to know if performance is limited by data quality or by model/software error. When systems become super complicated, it can be impossible to tell.<br><br><br><b><i>Dean Hall <dwhall256@gmail.com></i></b> wrote:<blockquote class="replbq" style="border-left: 2px solid rgb(16, 16, 255); margin-left: 5px; padding-left: 5px;"> Chris Jang, all,<br><br>I suggested back in November that using the NumberOfSatellites from <br>the NMEA stream of a GPS unit could provide an index into a lookup <br>table to get the variance value to use in a Kalman Filter (http:// <br>list.dprg.org/archive/2007-November/031055.html).<br><br>Well, today I stumbled across a
better idea. I was reading through <br>this article (http://www.cs.unc.edu/~welch/kalman/Levy1997/ <br>Levy1997_KFWorkhorse.pdf) that suggested on page 8 that pseudoranges <br>are preferred outputs to use in the KF. So I looked again through <br>the fields that my SiRFstarIII chip provides... nope, no <br>psuedoranges. However, serendipity befell me and I found the <br>Dilution of Precision (DOP) field in the GSA sentence and read up on <br>it at http://en.wikipedia.org/wiki/Dilution_of_precision_(GPS)<br><br>And there it was, in little black text near the bottom:<br><br>The DOP factors are functions of the diagonal elements of the <br>covariance matrix of the parameters, expressed either in a global or <br>a local geodetic frame.<br><br>I followed the reference wikipedia gave, read the article (http:// <br>www.developerfusion.co.uk/show/4652/2/) and found these two gems:<br><br> "As a general rule of thumb, an HDOP value of six or less is <br>recommended for
any application which makes suggestions to the user <br>based on the current location."<br><br> Accuracy of GPS Device * DOP = Maximum Allowable Error<br><br>I plan to use the HDOP in my KF. Also, note that the HDOP is usually <br>much smaller (=better) than the VDOP and mean DOP. Here are real- <br>world measurements I made: with 10 sats in view, I got a HDOP of 1.8; <br>with 12 sats (max for my GPS), HDOP = 0.9. Although I cannot confirm <br>it, it seems (from measuring and comparing to Google Earth) that *DOP <br>values have a unit of meters. So it looks like the HDOP value will <br>provide a nice input to calculate my GPS module's measurement error <br>variance. The downside is that the GSA sentence containing the HDOP <br>does not arrive as often as the RMC and VTG sentences in the default <br>configuration.<br><br>!!Dean<br>_______________________________________________<br>DPRGlist mailing
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