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Unsented Kalman Filter #28

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ChristopherRabotin opened this issue Jan 19, 2017 · 11 comments
Open

Unsented Kalman Filter #28

ChristopherRabotin opened this issue Jan 19, 2017 · 11 comments
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@ChristopherRabotin
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@blahmonkey
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Hi Christopher,

Any update on how this is coming along? Looking to use the UKF In a project, and there isnt an implementation in GoLang, so the only alternative is to use pykalman and go with (slow) python.

Thanks for your work!

@ChristopherRabotin
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ChristopherRabotin commented Mar 27, 2017 via email

@blahmonkey
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blahmonkey commented Mar 27, 2017

Hi,

We're planning to use it for aircraft tracking via multilateration, its the ideal way to smoothen the tracks. I need to look up the Tapley paper to see if there is anything that might render it unusable.

We will be building up data in 6 weeks or so, would that be too late for you? Also, would this kind of data work for you?

Thanks for your positive response!

Edit: oops, I guess its the Orbital Determination book. Very interesting (since there is much in common with multilateration methods). Will take a look :)

@ChristopherRabotin
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ChristopherRabotin commented Mar 27, 2017 via email

@blahmonkey
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Great, do update this thread once you have code to test.

Re EKF - thanks for clearing that up. Once we have some data, maybe we can do some testing with EKF as well.

@ChristopherRabotin
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I might only have this filter by the third or fourth week of April. I have a higher priority on implementing a square root information filter right now (which will be done this week, since I have a deadline for this in a week).

@blahmonkey
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Ok, thanks for letting me know! Should not affect our plans, and gives us time to get more data to test with.

@ChristopherRabotin
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@blahmonkey , just to keep you updated, I will be implementing a Scaled Square Root Unscented KF instead of a simple UKF. The main reason is that the UKF may quickly suffer from numerical issues leading the covariance to no longer be positive semi-definite. Moreover, the scaling allows for tuning the filter based on an initial guess of the distribution of the estimates, but can be turned off by setting the scaling to 1. In terms of performance, the square root version does not necessarily significantly require more computation power to work since the measurement update can be done using a Cholesky update. Actually, combining the Cholesky update and scaling, the performance should be on par with an EKF.

I was trying to find a publicly available document equivalent of the algorithm on which I'll be doing my implementation from. The UKF is not in Tapley, Schultz, Born. The reference I have is a presentation by Dr. Moriba Jah on that algorithm for Mars aerobraking at JPL.

@blahmonkey
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@ChristopherRabotin Thank you for the update! I've seen a paper on the Square Root UKF, I guess that is just this SSRUKF with scaling set to 1. Need to try it out and see how it works with our data

We've collected some data, just slowing down a bit to focus on other things, should be back to this track pretty soon. Good luck with your implementation, and keep me posted!

@ChristopherRabotin
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As a heads up, I'm not sure I'll get around to code up this UKF just yet. I initially wanted it for a project I have to give in on Tuesday, but I might skip this implementation.

@blahmonkey
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Ah, thats a pity. Sadly, our project is also delayed (its a commercial one) and we are also in a bit of uncertainty about it right now.

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