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I am trying to model a 1st order Gauss Markov Process (gyroscope drift). I know the Allan Variance characteristics of the process (bias instability, random walk characteristics).

I know the model should look like:

RandomFunction[SomeProcess[some_process_args], {some_time_characteristics}] 

but not quite sure where to start. I looked up ARProcess but it only seems to work with a unit time increment

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Cross posted it on dap.stackexchange…. –  Pam Mar 13 '13 at 16:48
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This question is too vague to meaningful to anybody other than an expert in Gauss Markov processes. You may get lucky and find one here, but otherwise you are going need to supply much more information in the form of Mathematica examples to elicit and answer. –  m_goldberg Mar 13 '13 at 17:57
    
Please do not cross-post to multiple sites. This is on-topic for Signal Processing, and your issue seems more fundamental than a specific Mathematica problem. I suggest deleting this and focusing on the DSP question. If you get a reply there and you have a specific model that you need help implementing in Mathematica, then post a question with all the relevant details and document your efforts. –  rm -rf Mar 13 '13 at 18:29
    
Sorry for the cross post…. I figured out a way to do this… in case anyone is in future… rate = RandomVariate[NormalDistribution[mean, stdevARW], time*f] + RandomFunction[WienerProcess[drift, volatility], {1/f, time, 1/f}]["Path"][[All, 2]]; –  Pam Mar 13 '13 at 21:04
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Answer your own question it's ok! –  Warren P Mar 14 '13 at 0:41

1 Answer 1

Answering my own question

I figured out a way to do this… in case anyone is interested…

rate  =  RandomVariate[NormalDistribution[mean, stdevARW], time] + RandomFunction[WienerProcess[drift, volatility], {1/f, time, 1/f}]["Path"][[All, 2]];
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