Trilateration example using least squares method in scipy.
Trilateration enables the unknown point to be found. However a since there are a number of samples a non linear least squares method needs to be used to find the solution that has the least error.
It is distinct from triangulation which has a series of angles to an unknown point. Trilateration uses a series of distances to an unkown point.
This code uses the scipy.optimize.least_squares method.
The first file, limited simulated points, takes 9 points which have a range to an unknown point. This was used as a test bed and can be manually manipulated to prove the method.
The second file, multiple simulated points, takes a large number of points, finds the closest n points and uses these in the least squares equations.
Equations.py is the output from the simulated script and is the next step in the implementation process.
Next step is to implement this code with real world data.