- #1
sibtain125
- 3
- 0
Hi all
1) When we are performing detection , and we have received this y(n)=x(n)+z(n) for n=0,1,2,3..., where z is gaussian noise, but about x(n) we don't know its distribution , all we know that it is I.I.D. random variable with zero mean and fxed given variance. Now my question is can we still perform the Neamen Pearson test . p(y;H1)/p(y;H0)>gamma in the same manner we do when x distribution is completely known.
2) If the answer to the above question is yes then will the pdf of p(y;H1) be gaussian with mean=0 and variance = (noise variance + signal variance).
3) Can you please refer me to useful texts where i can find how COST functions are modeled for signal detection systems.
regards
1) When we are performing detection , and we have received this y(n)=x(n)+z(n) for n=0,1,2,3..., where z is gaussian noise, but about x(n) we don't know its distribution , all we know that it is I.I.D. random variable with zero mean and fxed given variance. Now my question is can we still perform the Neamen Pearson test . p(y;H1)/p(y;H0)>gamma in the same manner we do when x distribution is completely known.
2) If the answer to the above question is yes then will the pdf of p(y;H1) be gaussian with mean=0 and variance = (noise variance + signal variance).
3) Can you please refer me to useful texts where i can find how COST functions are modeled for signal detection systems.
regards