Near-certainty confidence limits on GNSS positions
I was discussing with a friend the application of GNSS in the transportation industry. The forward direction accuracy might satisfied by a common car GPS unit, or perhaps some modest improvement would be beneficial.
The difficult requirement is to know what lane, or which of parallel railroad tracks, it is in with near certainty. Thus, in the lateral direction, rms (sigma) or 95% confidence values are mostly irrelevant. The spec is in terms of how many nines you can put in 99.99??% confidence at a chosen distance on the order of a meter or two.
There is some fear that, out in the tails of the distribution, the gaussian normal model won??t apply and you can??t depend on getting 99.999% at 4.42 sigma, or whatever you would calculate. In other disciplines, statistical tails have often been found to not fit the normal model.
Time averaging must play some part in this. A near-real-time result is needed, but there may be benefit for some applications in allowing a lag of a few seconds that would help fight multipath ??excursions? in the reported position.
Has anyone seen actual statistics on the extremes of GNSS position data?
Would something similar to this performance be possible with corrections from CORS or would it take closer-spaced reference stations?
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