Can this algorithm be used for 3D tracking of aircraft

[bhavikKhatau]

Is this implementation of kalman filter algorithm can be implemented for 3D aircraft tracking and gives output in X, Y & Z in cartesian system or range, azimuth & elevation in polar system.

[mannyray]

The license says:

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.

so be careful if tracking real aircraft...

With the "small print" out of the way, yes you can use the Kalman filter here to track aircraft in X,Y,Z. You need to provide:

• A(x): your expected model of motion of your aircraft. A(x) is some function dx/dt = A(x) where x = [X,Y,Z] and dx/dt is element wise time derivative of each element
• Q: expected process noise. If you don't know what you A(x) is you can compensate by increase this matrix
• C: matrix which determines how your measurement is computed based on the actual state [x,y,z]
• R: expected sensor noise. The noise on the sensor that is measuring the location.

The (Extended) Kalman filter can be used for any model including aircraft... as long as you can model it. If you are having issues coming up with a precise model for your specific situation then I recommend reading https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python. This will provide a good intuition behind A(x),Q,C,R. After, you can check the README of this repository on how to translate your A(x),Q,C,R arguments to match the functions defined in this repository.