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I am working on project for clustering of air objects based on their trajectories. Like I want to train a model on dataset of different flying object's trajectories so later I can predict what type of object is based on trajectory data. Now trajectory data include 4 things (Altitude, Longitude, Latitude, Time). So based on set of such dataset we may be able to classify objects like plane, rocket, missile etc. What I cant figure out is which algorithms can be used? I first thought about SVM. Later I thought "Long Short Term Memory" can be used. But I am not very sure. And I am new to machine learning. SO any help is appreciated.

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  • $\begingroup$ What input data to you have, 3-variable time series of latitude, longitude, and altitude at various times? $\endgroup$ – Dave Mar 11 at 15:18
  • $\begingroup$ @Dave Yes exactly. $\endgroup$ – thisisjaymehta Mar 11 at 15:31
  • $\begingroup$ Have a look at this competition kelvins.esa.int/collision-avoidance-challenge $\endgroup$ – Carlos Mougan Apr 12 at 14:24
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Depending on the amount of data, any classification algorithm can be suitable. LSTM, however, are likely to be an overkill, considering that you probably won't be having much variation in the time series for each object.

Instead of pondering about the algorithm, you'd better think of useful features you can extract from your data. My guess would be that speed, accelerations, and altitude would be most informative.

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  • $\begingroup$ How well do you think SVM can come handy? I mean will that help in classifying based of trajectory as well as other features as you mentioned like speed and acc. $\endgroup$ – thisisjaymehta Mar 11 at 15:53
  • $\begingroup$ It's impossible to say without knowing the data. Theoretically, SVMs are very powerful, especially when you don't have too much data (i.e. not millions of observations). On the other hand, they might be an overkill, too. I'd first take a look at the data, see how classes are distributed, whether they overlap and, if not, how far apart they are. For the start, I'd use a K-nearest-neighbors classifier. If that doesn't work, I'd try SVMs or Random Forests. Simple (not too deep) neural networks might also work, but probably not better than the former two. Deep NNs are most likely an overkill. $\endgroup$ – Igor F. Mar 11 at 16:16

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