如何执行地理定位以获取尽可能精确的数据？

Question

we are developing a multiplatform (android and ios) application in react-native, which mainly deals with geolocation data, thus it higly depends on accuracy and precision of the data. For instance, the application accumulates - reads and saves - the user's geolocation data every 5 seconds for ie 10 minutes (lets call it a track). So, during the track, there are about 200 measurements saved. Later on, we perform different computations based on the data accumulated during the track and visualise results to the user.

While on iphone the altitude graph of data measured at physically (nearly) identical alltitude has rather linear/smooth characteristics, on android there tend to be +-5 meters peaks. Lets say we want to measure total meters elevated during the track, thus to compute the sum of differences between succeeding measurements. Now imagine, how much the mentioned imprecision influences the result - there might be 5 meters altitude change every 5 seconds. Although physically your total elevation is +-0 meters, the total elevation computed may differ drastically.

So, to eliminate the errors, for us, there are basically two ways:

• to make the measurements more accurate and precise (this is natural)
• to perform some sort of approximation and adjust the measurements accordingly (this is hacky)

The first way sounds much better, but we are aware of the specific device's hardware limitation - we simply cannot fix inaccurate hardware with our software. The problem is, even on the same device, other commercial apps (ie Runtastic) perform the measurement "better" (even offline). That makes me think about the second (approximation) way. This way is rather hacky, and although it can lead to good-looking result in many cases, we think (but cannot prove) there is not such a prefrect approximation that will never corrupt the data "too much".

So, our questions are:

1. What are the techniques to make every altitude measurement as accurate and precise as possible on android? Which tools can be useful to accomplish that and how should we use them to get the best experience? We can think of:
   • gps altitude
   • nmea altitude
   • barometer
   • online API to get altitude based on latitude and longitude
2. Is it worth to apply some sort of approximation on the accumulated data? If so, what aspects (or even better ideas) should we take into account? Is this technique common?
3. Is there any different solution we did not mention?

Thanks in advance!

Answer 1

1. GPS altitude is the best one. but it depend on the gps chip. There are some with high accurate(High Cost). Most Phone GPS use online to get data also(For get good Hot start)

2. To Smooth GPS data(Or any other) ,You can use Kalman Filter. It complex and heavy but. it can guarantee the result and avoid misleading data very well.
   
   you can omit misleading packets base on pervious ones (Mean Filter) - Simple, But should be coded preciously

NOTE : I haven't use barometer. I gave my answer without it. Good luck

Answer 2

If you have a barometer then it's accuracy can be a lot better for altitude than you get with a phone quality gps. It's why high end bike computers and gps tracking watches have barometers in them. This is especially true if your interested in relative altitude differences rather than the actual real altitude. To get a good actual altitude with the barometer you need some sensible calibration scheme and be aware of the effects of the weather over time.

Whatever your source of altitude data you should expect to do some form of post processing on the data to get something sensible. What the processing will need to be will depend on your situation and what what you consider important.

For getting the total climb over a track you have 3 cases to consider. The easy case is simple going up or down a single consistent hill, you want to get the correct altitude difference value which is easy to check with a good map. At the other end going along a totally flat route you want to get zero climb again nice and easy to check but not so easy to achieve with typical hardware. In the middle going over undulating terrain is far harder to check although possibly the far more interesting case for the user. Getting this accurate is some form of trade off against the totally flat situation. I have code doing this in my app and with the filtering I have I know that the totally flat will over read a bit clocking up 20-30m climb in an hour. The undulating case will under count by about 1m for each undulation. The single big hill is generally pretty accurate given the known limitation of a barometric type system. Gps only based is nothing like as good. Typically going back to an online lookup approach is more consistent for a gps based system but it depends on the quality of the lookup data and the type of terrain.