GLOBAL NOISE ENVIRONMENT/COVARIANCE OF FREEDOM

[August Highland]

GLOBAL NOISE ENVIRONMENT/COVARIANCE OF FREEDOM


STRONG #0000001:

); i= T.  ****, The iterations (4.116) *.  \phi=q, method for the WSF algorithm related using oe.
It should be noted that the * obtained from the minimization.  (\delta ) represent the "in with
weighting matrix given by also to the WSF method, simply.





STRONG #0000002:

* 0 When an estimate of the noise.  Associated with each return Far large n, the distribution of M
oe.  0, stored as a product of E.  Convergence of the search ** normal distribution with.  Xx x x.





STRONG #0000003:

Weighted signal subspace, SA 1i.  *, s 2.  * accuracy in realistic _ TEf.  The deterministic maximum
\Lambda.  Deficient. the small sample, concept of generic I (B.7).





STRONG #0000004:

Extensively. this has resulted, underwater environment, the Example 4.1 Estimation.  S, )]=A(` test
(GLRT), see e.g. [55]..  Predict the actual variance, W (`)X.  H H.  ^oe, * applications only array.





STRONG #0000005:

Variables, that the AP initialization 1.  Xx xx xx demonstrated later..  1 =d and let N.  [64] it is
shown that if fl= )V *.  Yh, technique in the statistical 6 dB for the direct path and 3.





STRONG #0000006:

Ffl thus, invoke multivariate extreme value, )# ^ E.  The signal subspace dimension of the scheme
was proved in ^ `;.  I, x parameters can be determined.  ` -values to examine interesting to observe
that.  * d\Gamma 1.





STRONG #0000007:

X x xx xx x E.  Between adjacent elements is ^.  X unacceptable interference. The interest in sensor
array.  Assessed by, conditional) model is H.  Attraction" depends s (asymptotically) achieves the.





STRONG #0000008:

Improvement could be found \Gamma 1 0.  X xx, 2 i.  ^ ` \gamma ` xx xx **.  J analysis suggests
other (1=.  I, H ~ T.





STRONG #0000009:

^ boundaries between layers in.  H ^ S(`)A.  (t) impinging on the array. By well-known properties
of.  ^ ` be an asymptotically, j =O.  White and also independent H section dealt with optimal (in.





STRONG #0000010:

U applied, where ` identifiable (PI), i.e.,.  H ) that Tr(P.  H, Identifiability Under the ).  Z ^
`; x x.  After some manipulations weights. However, for i.





STRONG #0000011:

* N (* The "in-probability" rate of.  + p), then pi is guaranteed 2 ) : (B.3).  Seen that the
covariance of, The above equations define the A.  Fi fi fi fi fi i.  Not invertible and theorem 4.6
(`)=oe n.





STRONG #0000012:

1 ffl QR-decomposition using assumption. The Cram'er-Rao.  . in, T from sensor to sensor, i.e.,.  ~
u * o.  The estimation error variance, 2 P.  Propagation. an important, 1 jssoe _s(t \Gamma
o/ )=ff(t \Gamma.





STRONG #0000013:

K ) and o/ j is less than a specified.  (n, . The normalized log- projection matrix P.  2 3 4 5 6 7
8 9 10 prob, 1 2 6 4.  A, x * 3.  Ffi 1 n.





STRONG #0000014:

Basic data model is therefore, A sequence, x ?.  Convergence, fl(N ) H.  Of the two covariances must
be some applications, such as xx xx x xx xx xx xx.  H *.  ). methods that are.





STRONG #0000015:

H ^ u U.  Observation, a natural component of the parameter.  1, \Gamma 1 xx x xxx.  * that V.  :
(4:81) method if S is diagonal To determine the distribution.





STRONG #0000016:

)g)(x(t H.  O H.  Cram'er-rao lower bounds. the, D) fi S ^.  The earth are sensed by an, above as a
pd-dimensional s.  G have the same marginal, Z=I, since P performs is the covariance.





STRONG #0000017:

Y operations. The matrix Q is W.  Likelihood ratio is and TrfAg + TrfBg=TrfA + Bg. data model has
strong.  \gamma 1 k 21.  \gamma 1 is not only sufficient but.  @` eigenvalue of.





STRONG #0000018:

Xx xx xx xx 0 i.  X x, signal parameter estimate is method involves the evaluation.  Different cost
functions is, problem, corresponding to **.  1 : : : 1 e * wavefields are sampled both in.  Is
asymptotically equivalent, g OE(t \Gamma o/ )) ss ff(t).





STRONG #0000019:

Xx xx x is satisfied. : (4:124).  Subspace onto which it, A \Gamma 1.  G. each vector observation
is, , the normalized WSF cost W SF.  Asymptotically 65.  Reduced as m is increased. it, * (m \Gamma
d) squared random.




august highland

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