GNS/INS (Inertial Navigation System) integration

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SUMMARY

The discussion focuses on the integration of a GPS/Inertial Navigation System (INS) model, specifically addressing the challenges faced during GPS outages. The user has developed mechanization equations for the INS and implemented a Kalman filter to estimate position errors based on the differences between GPS and INS data. However, during GPS outages, the Kalman filter becomes ineffective due to the lack of measurements, prompting the need for alternative strategies. Suggestions include exploring inertial navigation techniques during GPS outages, as well as utilizing a Gauss-Markov process for noise modeling.

PREREQUISITES
  • Understanding of Kalman filter principles and applications
  • Familiarity with Inertial Navigation System (INS) mechanization equations
  • Knowledge of Gauss-Markov processes and their role in sensor noise modeling
  • Basic concepts of GPS technology and its integration with INS
NEXT STEPS
  • Research techniques for inertial navigation during GPS outages
  • Study advanced Kalman filter adaptations for scenarios with missing measurements
  • Explore the application of wavelet transforms in sensor noise reduction
  • Investigate the performance analysis of MEMS-based inertial sensors in conjunction with GPS
USEFUL FOR

Engineers and researchers working on navigation systems, particularly those integrating GPS with inertial sensors, as well as professionals seeking to enhance the reliability of navigation solutions during signal outages.

ramesses
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Hi
I'm working on a GPS/INS model.

Now I developped the mecanization equations for the INS.

I have a kalman filter which estimate the error state (position error with respect to north and east).

it takes as an input the diffrence between GPS and INS and gives its estimation .

I have developped a gauss-markov process which very similar to sensor noise (after applying wavlet).

Now, I don't know what can I do during the GPS outage ?

Because the mesaures are not available during this periode; I can't use Kalman filter.

Documentation that I have, doesn't say clearly what to do during GPS outage.
 
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ramesses said:
Hi
I'm working on a GPS/INS model.

Now I developped the mecanization equations for the INS.

I have a kalman filter which estimate the error state (position error with respect to north and east).

it takes as an input the diffrence between GPS and INS and gives its estimation .

I have developped a gauss-markov process which very similar to sensor noise (after applying wavlet).

Now, I don't know what can I do during the GPS outage ?

Because the mesaures are not available during this periode; I can't use Kalman filter.

Documentation that I have, doesn't say clearly what to do during GPS outage.

I googled Inertial Navigation During GPS Outage, and got some pretty good hits. Maybe have a look through some of them to see if they give you some ideas...

https://www.google.com/search?sourc...s+outage&gs_l=hp...0.0.0.7637...0.uCHzFb-uDEc

:smile:
 
ramesses said:
Now, I don't know what can I do during the GPS outage ?
During the GPS outage your GPS fix has infinite, or perhaps some really large, variance. So what happens to your Kalman filter when that happens?
 
@berkeman
Thank you :)
olivermsun said:
During the GPS outage your GPS fix has infinite, or perhaps some really large, variance. So what happens to your Kalman filter when that happens?
Kalman filter doesn't work until a new GPS's measure comes. unless the Kalman filter can't be used
 
Last edited:
Doesn't your Kalman filter have at least one input which is the integrating model from your INS?
 
Hello;
the system starts its loop after receiving the initiale position, bearing and speed from the GPS receiver.
this loop, presented in the first figure, is used during GPS outage.
2B4qVqQZVPD0.png

Now, when a new measure comes from a the GPS receiver, the distance between the GPS and INS positions will be used as input for the kalman filter.
2B4qkWENiM1S.png

the measurement equation, as in [1],is
2B4r14BBjQsU.png


I followed the same model as ([1]), except I used Gauss-Markov process instead of autoregression.
2B4quCJX5Css.png

The vector in the right is called the error state vector. It is used to estime the INS error.
αuN and αvN are the parameters of the 4 ordre autoregression process for the 2 accelerometer axes.

If I have well understand, the use of the process parameters inside this matrix serve for the prediction step. it gives an estimation for the evolution of the error for the epoch t+(1/INS_frequency)
but after giving its feedback, the error state vector will reset to zeros

What do you think about it ?

Sorry for my english

[1]Park, Minha, and Yang Gao. "Error and performance analysis of MEMS-based inertial sensors with a low-cost GPS receiver." Sensors 8.4 (2008): 2240-2261.
link to document: http://www.mdpi.com/1424-8220/8/4/2240/htm
link to PFE document: http://www.mdpi.com/1424-8220/8/4/2240/pdf
 
your opinion is very important to me, because I'm not very sure, that I used kalman filter correctly.
thank you
 

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