Algorithm 1: Unconstrained nonlinear optimization procedure.

Input: Measured dataset ${\left\{(x_i,y_i)\right\}}_{i=1}^m$

1: Use the mathematical model defined by Equation (2)

2: Determine the specific objective function $F(\Theta )$ to be minimized through Equation (3)

3: Calculate the residual vector $r_i(\Theta )=y_i-g(x_i,\Theta )$

4: Determine the Jacobian matrix $J_r(\Theta )$

5: Use a Non-linear Least Squares algorithm to estimate the optimal parameters as described in Equation (5)

Output: The vector parameter $\Theta$