Signal model decision classification between two possbile candidatesbased on the received signal vector?
Signal model decision between possbile candidates based on the received signal vector?
How to decide the most possible signal model between two model candidates besed on the received signal vector?
Assume the received signal vector is $y$, the possible signal model candidates could be:
(1) $y = Ax+n$, or
(2) $y = Bx+n$,
in which $x$ is the transmitted signal vector, and $A$ and $B$ are the system matrices for signal model candidate-1 and candidate-2 respectively, and $n$ is the Gaussian noise vector.
If $y$,$A$ and $B$ are all known, and the noise covariance matrix is $E[nn^H] = w^2I$, in which $w^2$ stands for noise power, and $I$ is the identity matrix, how to decide the most possible signal model candidate between the two. What's the optimal solution?
Thanks for any discussions.