>
>
>kalore wrote:
>
>> Hi guys
>> I'm trying to find a solution for detecting a sinusoidial signal under
>> non-gaussian noise, for example sinusoidial noise, consider the
following
>> model:
>> y1(t)=A*exp(j*w*t)+B*exp(j*w*t)+n(t)
>> y2(t)=B*exp(j*w*t)+n(t)
>> I want to detect if y1 or y2 is present, do you think that ML detection
is
>> applicable here (p(r/y1)/p(r/y2)><Th)?
>> how do I calculate the ML solution here?
>> Thanks, Kal
>> 
>> 
>> 
>> 

I think you can do it, but it is not required. . Follow the standard ML
derivation.Y1 will have the distribution at A+B and y2 will have at B.
You can derive from here...


Regards
Hozefa
>

what kind of information is missing?

On Mon, 26 Sep 2011 09:56:26 -0500, kalore wrote:

> Hi guys
> I'm trying to find a solution for detecting a sinusoidial signal under
> non-gaussian noise, for example sinusoidial noise, consider the
> following model:
> y1(t)=A*exp(j*w*t)+B*exp(j*w*t)+n(t)
> y2(t)=B*exp(j*w*t)+n(t)
> I want to detect if y1 or y2 is present, do you think that ML detection
> is applicable here (p(r/y1)/p(r/y2)><Th)? how do I calculate the ML
> solution here? Thanks, Kal

I don't know.  "A solution" to your detection problem is s = 0 -- but 
that's not what you want.

Maximum likelihood works if you know something of the nature of the 
noise.  Yes, maximum likelihood _may_ work, but whether it'll be good 
enough depends on a whole world of stuff that you haven't told us.

-- 
www.wescottdesign.com

kalore wrote:

> Hi guys
> I'm trying to find a solution for detecting a sinusoidial signal under
> non-gaussian noise, for example sinusoidial noise, consider the following
> model:
> y1(t)=A*exp(j*w*t)+B*exp(j*w*t)+n(t)
> y2(t)=B*exp(j*w*t)+n(t)
> I want to detect if y1 or y2 is present, do you think that ML detection is
> applicable here (p(r/y1)/p(r/y2)><Th)?
> how do I calculate the ML solution here?
> Thanks, Kal
> 
> 
> 
> 

Hi guys
I'm trying to find a solution for detecting a sinusoidial signal under
non-gaussian noise, for example sinusoidial noise, consider the following
model:
y1(t)=A*exp(j*w*t)+B*exp(j*w*t)+n(t)
y2(t)=B*exp(j*w*t)+n(t)
I want to detect if y1 or y2 is present, do you think that ML detection is
applicable here (p(r/y1)/p(r/y2)><Th)?
how do I calculate the ML solution here?
Thanks, Kal