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Detection, Estimation modulation theory part 1 - Vantress H.

Vantress H. Detection, Estimation modulation theory part 1 - Wiley & sons , 2001. - 710 p.
ISBN 0-471-09517-6
Download (direct link): sonsdetectionestimati2001.pdf
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To find the worst case we choose s(t) to make Ad as negative as possible.
2. Let the actual noise be
a(0 = n(t) + n(t) (322a)
whose covariance function is
Kna(t, u) = Kn(t, u) + Kn(t, ), (322b)
We assume that ne{t) has finite energy in the interval
E ' dt < Ą. (323a)
This implies that
Tf
J J KJ{t, ) dt du < . (323b)
Tt
To find the worst case we choose Kn(t, u) to make Ad as negative as possible.
Various other perturbations and constraints are also possible. We now consider a simple version of the first problem. The second problem is developed in detail in [42].
We assume that the noise process is stationary with a spectrum Sn(co) and that the observation interval is infinite. The optimum receiver, using
a whitening realization (see Fig. 4.38a), is shown in Fig. 4.48a. The
328 4.3 Detection and Estimation in Nonwhite Gaussian Noise
(a)
2
E(l\H0)
7
VEs(t)
- Decision line
Var [l\H0]=d2
--------H
(1\)
(b)
2
^Decision line
r
\
, V..
7
(c)
Fig. 4.48 Sensitivity analysis: (a) filter with nominal input; (b) nominal decision space;
(c) actual design space.
Sensitivity 329
corresponding decision space is shown in Fig. 4.48. The nominal performance is
/* oo
?(/|,) - ,)
- [Var(/|0)] - (
or
-I y2
d = [J CO S*(0 dt\ (325)
We let the actual signal be
sa(t) = VE s(t) + VE se(t), oo < t < , (326)
where s(t) and se(t) have unit energy. The output of the whitening filter will be
r*a(r) A s*(t) + 5?(0 + *(0> - < t < oo, (327)
and the decision space will be as shown in Fig. 4.48c. The only quantity that changes is ?(/|߳). The variance is still the same because the noise covariance is unchanged. Thus
Ad = -d J" **(#) **(0 dt. (328)
To examine the sensitivity we want to make Ad as negative as possible. If we can make Ad = d, then the actual operating characteristic will be
the PD = PF line which is equivalent to a random test. If Ad < d, the
actual test will be worse than a random test (see Fig. 2.9a). It is important to note that the constraint is on the energy in s(t), not s*f(t). Using Parsevals theorem, we can write (328) as
dw
Arf = J^ S*<(>) SJO'o>) (329)
This equation can be written in terms of the original quantities by observing that
SUM = VFt Hw(jco) SeO) (330)
and
Thus
A d =
S0(jw) = Vehju*) SO)- (331)
Vee]
d
j*_ s*0)
VEE-<**>
330 43 Detection and Estimation in Nonwhite Gaussian Noise
The constraint in (321) can be written as
|seO)|2^ = 1. (333)
To perform a worst-case analysis we minimize subject to the constraint in (333) by using Lagrange multipliers. Let
F=Ad+ A [J^ |Se(|2 ? - l] (334)
Minimizing with respect to S(joj), we obtain
9 (ioA = ^EEz S(ja)
Se0U>) 2Xd (335)
(the subscript denotes optimum). To evaluate A we substitute into the constraint equation (333) and obtain
-*,,
4WJ-. 2 ' ,,m>
If the integral exists, then
VIE,
2A =
d
Substituting into (335) and then (332), we have
"--<->
(Observe that we could also obtain (338) by using the Schwarz inequality in (332).) Using the frequency domain equivalent of (325), we have
rrr _2^/2]
^ _ _ (<\ V U 2ni [ m<n
d~ U) rr ^
L L J - oo 5(<o) 2 J J
In the white noise case the term in the brace reduces to one and we obtain the same result as in (82). When the noise is not white, several observations are important:
1. If there is a white noise component, both integrals exist and the term in the braces is greater than or equal to one. (Use the Schwarz inequality on the denominator.) Thus in the colored noise case a small signal perturbation may cause a large change in performance.
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