I've done some work with GPS and INS systems, and essentially what an extended Kalman filter (GPS/INS data is not linear so you can't use a linear function to represent it, and this is what the EKF was invented for correcting) is a way of correcting a measured value with an estimate. I can estimate position based on data from an accelerometer, and then since I know the noise distribution of my estimate (based on accelerometer specs) and I know the noise distribution of GPS data (based on GPS specs) I can correct my location with the estimated position, and weight them appropriately in varied conditions.
>From what I have seen with G1 GPS data is that it is very poor quality, the only time I get a GPS lock is when I have a clear sky with a great deal of constellation visibility (hard to say for certain since I've only looked at this data as a user, rather than seeing how many satellites the receiver can hear, and how well it can hear them). I am completely unaware of what accelerometer data is available to an andriod developer, or its quality. If you truly just want to smooth the data, you could simply take the mean of the GPS data that you're getting, and that will deal with outliers pretty well, if you want to really accurately estimate your position in the case of low signal level, and possible GPS lock loss you could try and implement an EKF, based on the GPS data and the accelerometer data (I've been thinking about doing this but am not sure when I'll have the time). This site: http://www.cs.unc.edu/~welch/kalman/ Has some great information on Kalman filters. On Dec 8, 7:06 pm, Brian Abbott <[EMAIL PROTECTED]> wrote: > Interesting. I did some R&D/Simple prototype coding for a WiFi Indoor > Location Positioning System in 2001/2. I experimented with Kalman > filters as well as RBFs in order to calm down the random jumping that > occurs with indoor non-triangulated positioning. I say non- > triangulated because, in truth it wasnt, and I assumed such problems > would've disappeared had we been using Angle-of-Arrival or Time-of- > Flight methods, similiar to GPS. I'd never heard of GPS jumping or > giving inaccurate positions in the scenario of a weak signal however, > after having gone through the task of WiFi LPS, I dont doubt it, RF is > a strange place that can through some interesting curve balls at > you. > > It's a hard problem to solve if it's the case that GPS could give rise > to the same issues. I'm sure with Cellular-Enhanced GPS, it becomes > more likely. However, in order to implement something like this, I > believe you would need decibal data from the GPS/Cellular Receievers > for each signal incorporated into the measurement in order to weight > each reading, I dont believe you would want to throw all readings in > any kind of a filter equally weighted. Most of the time, say when the > average Californian is driving on a busy roadway or highway, you > should be able to receive a clean signal since, in terms of an average > over distance traveled, you dont have too many tall buildings. Of > course the anecotal response to that is that the large asian cities is > where most users of these systems will probably reside however, the > truth remains that good signals shouldnt be weighted the same as > something that had poor signal quality to begin with. Although it's > been.. six years since I've looked at Kalman Filters... time to > refresh! I'm not sure if it's a problem but, It will be interesting to > see what kind of problems come up with regard to positioning > availability and accuracy! I think their will probably be a need for > hybrid systems with something like Zigbee or, a kind of ultra- > sensitive custom indoor time-of-flight/AoA system as people begin to > realize the value of Location and not only demand to be tracked but, > down to the foot, especially in heavily populated environments.. in > say 5 years? > > Cheers, > > Brian A. > > On Dec 7, 2:12 pm, [EMAIL PROTECTED] wrote: > > > hi all, > > > so i understand that there are lots of smart people at google, so > > maybe maybe this is > > already in shipping libraries :) > > > My guess is that the GPS data coming back from the my android is just > > raw, unmassaged > > data as it gets it from whatever chipset the T1 is using (just like > > what happens in the > > emulator :). As people I'm sure realize, GPS data is really really > > noisy, especially when > > the signal is weak, etc. > > > I've been googling around and it seems like Kalman filters for GPS > > would be a good > > way of doing curve fitting so that outliers and other nasties would > > get smoothed out. > > Does anybody know of either this filter, or some other GPS filtering > > mechanism that > > are either available through the API's, or elsewhere? > > > TIA, Mike --~--~---------~--~----~------------~-------~--~----~ You received this message because you are subscribed to the Google Groups "Android Developers" group. To post to this group, send email to android-developers@googlegroups.com To unsubscribe from this group, send email to [EMAIL PROTECTED] For more options, visit this group at http://groups.google.com/group/android-developers?hl=en -~----------~----~----~----~------~----~------~--~---
