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.
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.