On 03/23/2010 07:23 AM, JFrog wrote:
Rough, yeah. I'll have to look into the best way to throw out inaccurate values. Otherwise the application tends to look a bit ugly (inertial navigation). For instance, if I walk by a window while my application is estimating movements on its own and happens to get a gps signal for a moment or two, then all of a sudden I get one or two inaccurate dots come on the screen and my estimated movement for where the droid is will start at that new GPS location.
I don't know what your app is, but for mine (tracking skiing/riding, etc) I just filter out points with horizontal accuracy > 100m. This works just fine for me because I'm interested in the overall dataset, but I'm not terribly real time sensitive. But I also do some post-processing to deal with outliers like looking for silly velocities and accelerations. I had looked into smoothing the data with Kalman filtering, but it turned out that it wasn't necessary even though it would probably do a pretty good job.
At first I thought perhaps I would throw away a couple of initial values, but it doesn't seem as though there is a consistent number of bad results before receiving a good one. I'll see if there is a way to wait for a fine grained result related to triggering the onLocationChanged segment. Perhaps I can even put up a range zone around my estimated value, and as long as its close I can throw away any GPS that fall outside that range. That's my current thoughts.
The hdop and vdop are your friends :) Google them as it's the GPS measures that eventually get translated into meter-based accuracy. The bad results are based on the satellite constellation at the time, where the GPS signal processing state is, and of course noise like multipath, etc. In normal terrestrial environs it's just a fact of life that the accuracy is going to better at some times than others. Adjust accordingly. Mike
Thanks for the input Mike.
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