GNS/INS (Inertial Navigation System) integration

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Discussion Overview

The discussion revolves around the integration of GPS and Inertial Navigation Systems (INS), specifically addressing the challenges faced during GPS outages. Participants explore the mechanization equations for INS, the role of Kalman filters in estimating position errors, and alternative approaches to maintain navigation accuracy when GPS data is unavailable.

Discussion Character

  • Technical explanation
  • Exploratory
  • Debate/contested

Main Points Raised

  • One participant describes their development of mechanization equations for INS and a Kalman filter that estimates position error based on the difference between GPS and INS data.
  • Another participant questions the behavior of the Kalman filter during GPS outages, noting that the GPS fix may have infinite or very large variance, which could affect the filter's functionality.
  • A different participant suggests that the Kalman filter may not work until new GPS measurements are received, raising concerns about its usability during outages.
  • One participant mentions that the system begins its loop after receiving initial position, bearing, and speed from the GPS, and describes how the distance between GPS and INS positions is used as input for the Kalman filter when new measurements are available.
  • Another participant expresses uncertainty about the correct application of the Kalman filter and seeks feedback on their approach, particularly regarding the use of a Gauss-Markov process in their model.

Areas of Agreement / Disagreement

Participants express uncertainty regarding the operation of the Kalman filter during GPS outages, with no consensus reached on the best approach to maintain navigation accuracy in such scenarios. Multiple competing views on the filter's functionality and alternative strategies are present.

Contextual Notes

Participants reference specific models and processes, such as the Gauss-Markov process and autoregression, but the discussion lacks clarity on the assumptions and limitations of these methods, particularly in the context of GPS outages.

Who May Find This Useful

This discussion may be useful for individuals working on GPS/INS integration, particularly those interested in the challenges of maintaining navigation accuracy during GPS outages and the application of Kalman filters in such contexts.

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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