Table 2.

Waveform models used in timing estimation

Waveform models	Definition	No. of model parameters
Double exponential	$Q(t)=\{\begin{array}{cc}{\lambda }_1·t+{\lambda }_2,\hfill & t<t_0\hfill \\ ({\lambda }_1·t+{\lambda }_2)+{\lambda }_3·{\lambda }_4·[1-\text{exp}(-\frac{t-t_0}{{\lambda }_4}\left)\right]\hfill & t\ge t_0\hfill \\ +{\lambda }_5·{\lambda }_6·[1-\text{exp}(-\frac{t-t_0}{{\lambda }_6}\left)\right],\hfill & \hfill \end{array}$	7 (λ1, λ2, t0, λ3, λ4, λ5 and λ6)
Single exponential	$Q(t)=\{\begin{array}{cc}{\lambda }_1·t+{\lambda }_2,\hfill & t<t_0\hfill \\ ({\lambda }_1·t+{\lambda }_2)+{\lambda }_3·{\lambda }_4·[1-\text{exp}(-\frac{t-t_0}{{\lambda }_4}\left)\right],\hfill & t\ge t_0\hfill \end{array}$	5 (λ1, λ2, t0, λ3 and λ4)
Linear	$Q(t)=\{\begin{array}{cc}{\lambda }_1·t+{\lambda }_2,\hfill & t<t_0\hfill \\ {\lambda }_3·t+[({\lambda }_1-{\lambda }_3)·t_0+{\lambda }_2],\hfill & t\ge t_0\hfill \end{array}$	4 (λ1, λ2, t0 and λ3)