Algorithm 1: Generation Procedure of Distribution ${\Upsilon }_d\left(m\right)$ and Distribution ${\Upsilon }_f\left(m\right)$
1:	Input: The detection probability set ${\mathcal{P}}_d$ and the false alarm probability set ${\mathcal{P}}_f$.
2:	Output: The detection probability distribution ${\Upsilon }_d\left(m\right)=\left\{{\Upsilon }_1^d\left(m\right),{\Upsilon }_2^d\left(m\right),\cdots ,{\Upsilon }_{\xi }^d\left(m\right)\right\}$
	The false alarm probability distribution ${\Upsilon }_f\left(m\right)=\left\{{\Upsilon }_1^f\left(m\right),{\Upsilon }_2^f\left(m\right),\cdots ,{\Upsilon }_{\varsigma }^f\left(m\right)\right\}$.
	The detection probability distribution generation:
1:	Sort the detection probability $p_d\left(m,1\right),p_d\left(m,2\right),\cdots ,p_d\left(m,N_c\right)$ in ascending order and constitute the sorted sequence as ${\widehat{p}}_d\left(1\right),{\widehat{p}}_d\left(2\right),\cdots ,{\widehat{p}}_d\left(N_c\right)$.
2:	$\forall X_d,Y_d>0$, for $X_d<{\widehat{p}}_d\left(1\right)$ and $Y_d>{\widehat{p}}_d\left(N_c\right)$.
3:	Divide interval $\left[X_d,Y_d\right]$ into $\xi$ equal subintervals, i.e., $X_d={\mu }_0^d<{\mu }_1^d<{\mu }_2^d\cdots <{\mu }_{\xi -1}^d<{\mu }_{\xi }^d=Y_d$.
4:	for $\ell =1\to \xi$ do
5:	${\mu }_{\ell }^d-{\mu }_{\ell -1}^d=\left(Y_d-X_d\right)/\xi$.
6:	Calculate the number of the detection probabilities within subinterval $\left({\mu }_{\ell -1}^d,{\mu }_{\ell }^d\right]$ denoted by $n_{\ell }^d$.
7:	Calculate the probability ${\Upsilon }_{\ell }^d\left(m\right)=n_{\ell }^d/N_c$.
8:	end for
9:	Return: ${\Upsilon }_d\left(m\right)=\left\{{\Upsilon }_1^d\left(m\right),{\Upsilon }_2^d\left(m\right),\cdots ,{\Upsilon }_{\xi }^d\left(m\right)\right\}$.
	The false alarm probability distribution generation:
1:	Sort the false alarm probability $p_f\left(m,1\right),p_f\left(m,2\right),\cdots ,p_f\left(m,N_c\right)$ in ascending order and constitute the sorted sequence as ${\widehat{p}}_f\left(1\right),{\widehat{p}}_f\left(2\right),\cdots ,{\widehat{p}}_f\left(N_c\right)$.
2:	$\forall X_f,Y_f>0$, for $X_f<{\widehat{p}}_f\left(1\right)$ and $Y_f>{\widehat{p}}_f\left(N_c\right)$.
3:	Divide interval $\left[X_f,Y_f\right]$ into $\varsigma$ equal subintervals, i.e., $X_f={\mu }_0^f<{\mu }_1^f<{\mu }_2^f\cdots <{\mu }_{\varsigma -1}^f<{\mu }_{\varsigma }^f=Y_f$.
4:	for $\ell =1\to \varsigma$ do
5:	${\mu }_{\ell }^f-{\mu }_{\ell -1}^f=\left(Y_f-X_f\right)/\varsigma$.
6:	Calculate the number of the false alarm probabilities within subinterval $\left({\mu }_{\ell -1}^f,{\mu }_{\ell }^f\right]$ denoted by $n_{\ell }^f$.
7:	Calculate the probability ${\Upsilon }_{\ell }^f\left(m\right)=n_{\ell }^f/N_c$.
8:	end for
9:	Return: ${\Upsilon }_f\left(m\right)=\left\{{\Upsilon }_1^f\left(m\right),{\Upsilon }_2^f\left(m\right),\cdots ,{\Upsilon }_{\varsigma }^f\left(m\right)\right\}$.