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Forum index » Science and Technology » Engineering » Control
Glitch/inconsistency detection in navigation data
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Rune Allnor
science forum beginner


Joined: 03 May 2005
Posts: 18

PostPosted: Mon Jun 26, 2006 11:45 am    Post subject: Re: Glitch/inconsistency detection in navigation data Reply with quote

Andor wrote:
Quote:
Rune Allnor wrote:
Hi folks.

Assume you are given som 20,000 - 100,000 points of raw (x,y,z)
navigation data as previously logged on some platform, and are asked
to clean up the data.

My first, naive idea is to use some Kalman filter. So far so good.

However, the data might contain glitches (jumps) or other
inconsistencies
that need to be sorted out before feeding them to the Kalman filter.

How would you go about detecting glitches and maybe even do a
first-pass correction of such data? A manual check of 100,000 data
points doesn't seem as a very tempting prospect...

What do you look for in a manual check?

Anything that can be said to be a "glitch" or an "irregular feature".
Some of the tracks seem to "jump" sideways on the order of 2Dx
to 3Dx, where Dx is the along-track distance between measurements.
Then they get back "on-track" within a variable number of samples,
say 5 to 20 samples.

I have a couple of ideas about how to *detect* these things, for
manual correction. But then, if there already exists a wheel, why
re-invent it...

Rune
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Andor
science forum beginner


Joined: 01 Jul 2005
Posts: 6

PostPosted: Mon Jun 26, 2006 11:38 am    Post subject: Re: Glitch/inconsistency detection in navigation data Reply with quote

Rune Allnor wrote:
Quote:
Hi folks.

Assume you are given som 20,000 - 100,000 points of raw (x,y,z)
navigation data as previously logged on some platform, and are asked
to clean up the data.

My first, naive idea is to use some Kalman filter. So far so good.

However, the data might contain glitches (jumps) or other
inconsistencies
that need to be sorted out before feeding them to the Kalman filter.

How would you go about detecting glitches and maybe even do a
first-pass correction of such data? A manual check of 100,000 data
points doesn't seem as a very tempting prospect...

What do you look for in a manual check?
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Eino Uikkanen
science forum beginner


Joined: 26 Jun 2006
Posts: 2

PostPosted: Mon Jun 26, 2006 11:06 am    Post subject: Re: Glitch/inconsistency detection in navigation data Reply with quote

"Rune Allnor" <allnor@tele.ntnu.no> kirjoitti
viestissä:1151317297.168309.116350@m73g2000cwd.googlegroups.com...
Quote:
Hi folks.

Assume you are given som 20,000 - 100,000 points of raw (x,y,z)
navigation data as previously logged on some platform, and are asked
to clean up the data.

My first, naive idea is to use some Kalman filter. So far so good.

However, the data might contain glitches (jumps) or other
inconsistencies
that need to be sorted out before feeding them to the Kalman filter.

How would you go about detecting glitches and maybe even do a
first-pass correction of such data? A manual check of 100,000 data
points doesn't seem as a very tempting prospect...

Rune

I don't know about the systems to exclude irregularities, I doubt if there
are any without some presumptions, e.g. maximum distance between two
consecutive points etc. I will stay tuned to here about such methods.

However, if you after getting rid of the exceptions (errors in data) want to
reduce the number of points in your navigation data, you could apply my
freeware GeoConv. With GeoConv you can reduce the number of points in your
navigation data still maintaining the shape of the track as close to the
original track as possible.

GeoConv: http://www.kolumbus.fi/eino.uikkanen/geoconvgb/index.htm

Eino Uikkanen
http://www.kolumbus.fi/eino.uikkanen/gb/index.htm
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Rune Allnor
science forum beginner


Joined: 03 May 2005
Posts: 18

PostPosted: Mon Jun 26, 2006 10:21 am    Post subject: Glitch/inconsistency detection in navigation data Reply with quote

Hi folks.

Assume you are given som 20,000 - 100,000 points of raw (x,y,z)
navigation data as previously logged on some platform, and are asked
to clean up the data.

My first, naive idea is to use some Kalman filter. So far so good.

However, the data might contain glitches (jumps) or other
inconsistencies
that need to be sorted out before feeding them to the Kalman filter.

How would you go about detecting glitches and maybe even do a
first-pass correction of such data? A manual check of 100,000 data
points doesn't seem as a very tempting prospect...

Rune
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