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I would like to increase the density of my AIS or GPS data in order to carry out more precise analyses afterwards. During my research I came across different approaches like interpolation, filtering or imputation. With the first two approaches, there is no doubt that these can be used to approximate the points between two collected data points. In the case of imputation (e.g. MICE), however, I have not yet found an approach in the literature for determining position data.

That's why I wanted to ask if anyone knew a paper dealing with this subject and whether it makes sense at all to determine further position data approximately by imputation.

Braveness
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  • This probably, depends on your data. Would you mind sharing, what it looks like and what your exact scenario is? Otherwise it is hard to give a accurate advice. From your current description interpolation seems best suited in my opinion. – Steffen Moritz Jul 19 '19 at 13:37
  • The structure of the data looks like: Latitude, Longitude, Speed, Course. So it's a four-dimensional imputation problem. My goal is to approximate the data between the measured gps points. So e.g. i have a datapoint at t = 5 and t = 10. And now i would like to know where the vehicle was at t = 7. – Braveness Jul 20 '19 at 17:08
  • Wouldn't the best option be to calculate the missing value then? I mean if you have speed, course and position at t=5 and assume the speed and direction doesn't change, wouldn't you be able to calculate your new position at t = 7? – Steffen Moritz Jul 22 '19 at 15:12
  • Ahh now I see - maybe google for "interpolation in 3d grids" or "Trilinear interpolation" this could be what you are looking for. – Steffen Moritz Jul 22 '19 at 15:15
  • thanks for your advice. i will try it out. – Braveness Jul 24 '19 at 10:29

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The problem you are describing there is trajectory reconstruction for AIS/GPS data. There's a number of papers for general trajectory reconstruction (see this for example), but AIS data are quite specific.

The irregularity of AIS data is a well know problem with no standard approach to deal with, as far as I know. However, there is a handful of publications which try to deal with this issue. The problem of reconstruction is connected to the trajectory prediction problem, since both of these two shares some of the methods (the latter is more popular in the scientific community, I think).

Traditionally, AIS trajectory reconstruction is done using some physical models, which take into account the curvature of the earth and other factors, such as data noise (see examples here, here, and here). More recent approach tries to use LSTM neural networks.

I don't know much about GPS data, but I think the methods are very similar to the ones mentioned above (especially taking into account the fact that you probably want to deal with maritime data).

Dominik Filipiak
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