[ntp:questions] [Thumbgps-devel] good paper on timing and delay
Eric S. Raymond
esr at thyrsus.com
Tue May 22 15:15:17 UTC 2012
Dave Hart <hart at ntp.org>:
> > So, the possibly simplistic question is, if our network time sync programs
> > used the same algorithm that the GPS receivers use to read their "servers",
> > ie satellites, which all have variable and perhaps somewhat asymmetric
> > propagation delays, which can be substantial, would we be able to achieve
> > much greater levels of accuracy doing synchronization via the internet?
> GPS birds tell the receiver the flight plans of all birds, so the
> receiver knows the dominant factor in the propagation delay, the
> distance between transmitter and receiver. With WAAS reception, even
> more information about delay is provided in the form of atmospheric
> conditions. NTP has a much more difficult row to hoe.
This is true, but Ron might nevertheless be onto something here.
The solver algorithm for position given a bunch of pseudoranges
is irrelevant to anything NTP does. It's straight-up spherical
GPSes also use Kalman filtering to try to back out the effects of
variable ionospheric delay, which manifests as noise in the
pseudorange measurements. The pseudorange noise behaves a lot like
jitter in network packet latencies.
It's possible that Kalman filtering could be useful for cleaning noise
from an NTP server's measurements of propagation delay. It's a general
technique used for all kinds of noisy time series.
The Kalman filter, also known as linear quadratic estimation (LQE), is
an algorithm which uses a series of measurements observed over time,
containing noise (random variations) and other inaccuracies, and
produces estimates of unknown variables that tend to be more precise
than those that would be based on a single measurement alone. More
formally, the Kalman filter operates recursively on streams of noisy
input data to produce a statistically optimal estimate of the
underlying system state.
<a href="http://www.catb.org/~esr/">Eric S. Raymond</a>
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