# Correct approach for multilateration planimetric survey

Not being a statistician I apologize in advance. With multilateration I mean a 2D geometric grid of distances measured with a laser-meter (affected by a random error), such as the one below:

I currently estimate the real distances numerically (using scipy Python package optimize.minimize function), by minimizing the objective function

$$\sum_{i=1}^{n} (De_i-Dm_i)^2$$

where $$Dm_i$$ are the $$n$$ measured distances and $$De_i$$ are the estimated real distances expressed in terms of vertices coordinates $$(x,y)$$

$$De = \sqrt{(x_b-x_a)^2+(y_b-y_a)^2}$$

which are the actual ultimate unknowns of the problem (at least with this approach).

I wonder the above approach is correct, and in the affirmative case if there is a way to find the solution directly (non numerically).