3
$\begingroup$

Assume that we have a signal to be detected using the binary testing hypothesis. The received signal is distributed as follows.
\begin{align} \mathcal H_0:& \ x = w\\[2ex] \mathcal H_1:& \ x = s + w \end{align} where $w\sim \mathcal N(\mu_w,C_w)$ and $s\sim \mathcal N(\mu_s,C_s)$

I was trying to apply the NP lemma, but I'm getting stuck somewhere.

$\endgroup$
  • $\begingroup$ getting stuck because $C_w \ne C_s$ ? $\endgroup$ – Stanley Pawlukiewicz Dec 28 '17 at 15:06
  • $\begingroup$ It's okay even if you take Cw and Cs as diagonal matrices! $\endgroup$ – Space crawler Dec 28 '17 at 15:09
  • $\begingroup$ so where stuck? $\endgroup$ – Stanley Pawlukiewicz Dec 28 '17 at 15:13
  • $\begingroup$ In calculating Pd and Pfa $\endgroup$ – Space crawler Dec 28 '17 at 15:14
  • $\begingroup$ try the scaler case first and then the independent case. The multidimensional integrals can be challenging. There are numerical papers using quadrature rules. There are some tricks in Abramawitz and Stegun. Chervov bounds can also be helpful $\endgroup$ – Stanley Pawlukiewicz Dec 28 '17 at 15:26
1
$\begingroup$

You're looking at a Generalized Gaussian Detector$^{\ \text [1]}$. The Neyman-Pearson detector decides $\mathcal H_1$ if: $$ \frac{p(\mathbf x; \mathcal H_1)}{p(\mathbf x; \mathcal H_0)}> \gamma\tag{1} $$ With $\mathbf w$ and $\mathbf x$ independent; you get here: $$\mathbf x\sim \begin{cases} \mathcal N\left(\mu_w, \mathbf C_w\right) &\text{under} \mathcal \quad \mathcal H_0\\[2ex] \mathcal N\left(\mu_s + \mu_w, \mathbf C_s + \mathbf C_w\right) &\text{under} \mathcal \quad\mathcal H_1 \end{cases}$$

The NP inequality in equation $(1)$ gives:

$$ \frac{\displaystyle\frac{1}{(2\pi)^{N/2}\mathrm{det}^{1/2}\left(\mathbf C_s + C_w\right)}\exp\left[-\frac{1}{2}\left(\mathbf x-\mu_s - \mu_w\right)^T\left(\mathbf C_s + \mathbf C_w\right)^{-1}\left(\mathbf x-\mu_s - \mu_w\right)\right]}{\displaystyle\frac{1}{(2\pi)^{N/2}\mathrm{det}^{1/2}\left(\mathbf C_w\right)}\exp\left[-\frac{1}{2}\left(\mathbf x-\mu_w\right)^T\mathbf C_w^{-1}\left(\mathbf x-\mu_w\right)\right]}>\gamma $$ Taking the logarithm on both sides gives the needed test statistic $T(\mathbf x)$: $$ \underbrace{\ln\left(\frac{\displaystyle\frac{1}{(2\pi)^{N/2}\mathrm{det}^{1/2}\left(\mathbf C_s + C_w\right)}\exp\left[-\frac{1}{2}\left(\mathbf x-\mu_s - \mu_w\right)^T\left(\mathbf C_s + \mathbf C_w\right)^{-1}\left(\mathbf x-\mu_s - \mu_w\right)\right]}{\displaystyle\frac{1}{(2\pi)^{N/2}\mathrm{det}^{1/2}\left(\mathbf C_w\right)}\exp\left[-\frac{1}{2}\left(\mathbf x-\mu_w\right)^T\mathbf C_w^{-1}\left(\mathbf x-\mu_w\right)\right]}\right)}_{T(\mathbf x)}>\ln(\gamma) $$ You can continue from there. Also, have a look at this related question on NP detector.

$^{\ \text [1]}$: Kay, S. Fundamentals of Statistical Signal Processing: Detection Theory.

$\endgroup$
  • $\begingroup$ How are $C_s$ and $C_w$ estimated from "real" data? $\endgroup$ – catch22 May 26 at 5:21

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.