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2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop (SAM)
SUBSPACE ESTIMATION USING FACTOR ANALYSIS
Ahmad Mouri Sardarabadi, and Alle-Jan van der Veen
Faculty of Electrical Engineering, Mathematics and Computer Science
Delft University of Technology (TU Delft)
e-mail:{A.Mourisardarabadi, A.j.veen}@tudelft.nl
ABSTRACT
Many subspace estimation techniques assume either that the system has a calibrated array or that the noise covariance matrix is
known. If the noise covariance matrix is unknown, training or
other calibration techniques are used to find it. In this paper
another approach to the problem of unknown noise covariance
is presented. The complex factor analysis (FA) and a new extended version of this model are used to model the covariance
matrix. The steep algorithm for finding the MLE of the model
parameters is presented. The Fisher information and an expression for the Cramér–Rao bound are derived. The practical use of
the model is illustrated using simulated and experimental data.
Index Terms— Factor analysis, complex factor analysis, subspace estimation, Cramér-Rao bound, maximum-likelihood.
1. INTRODUCTION
Many array processing techniques rely on estimating the signal subspace using eigenvalue decomposition (EVD) of the covariance matrix. This method does not have an inherit model for the noise and
its usage for subspace estimation is limited to the case where the
noise covariance matrix could approximately be modeled as σ 2 I. In
this paper we are interested in estimating the signal subspace even
if the noise covariance does not have this special form and is unknown. Also each receiving element of the array could have a different unknown gain. These complications requires a data–model
that includes unknown noise powers and is scale–invariant. The FA
model has both of these required properties and is used to find the
desired subspace.
After its first formulation by Spearman in 1904, the FA model
has been used in various fields such as psychology, social sciences,
natural science, etc [1, 2, 3]. Variations of the FA model have also
been explored for blind source separation and array calibration [4, 5].
Even though the work presented here could be used in various
fields, the focus is on its usage for radio–astronomy. Spatial filtering of the strong sources or interference and removing the extended emissions are two possible applications of the FA model in
this field. This paper gives some new results needed for extending
the FA model to complex case and also extends the model to account
for a more general noise models. The MLE of the model parameters
is estimated using the steep method. The Fisher information and the
Fisher score are presented here could also be used for the scoring
algorithm as presented in [6].
Where x is a p × 1 vector of received signals, A0 is the p × m arrayresponse matrix, s0 is an m × 1 vector of source signals and n is a
p × 1 vector representing all the noise contributions in the system.
This data model suffers from some ambiguities that need to
be addressed before attempting to estimate the model parameters.
Given any invertable matrix Z and any unitary matrix Q, the model
could be rewritten as
x(t) = A0 ZQQH Z −1 s0 (t) + n(t).
(2)
It is assumed that the sources and noise contributions are uncorrelated and have proper complex Gaussian distributions CN (0, Rs0 )
and CN (0, Rn ) respectively. In this situation the covariance matrix
of received signal is given by
Rx = A0 ZQQH Z −1 Rs0 Z −H QQH Z H AH
0 + Rn .
(3)
The matrix Z could always be chosen in such a way that the
Z −1 Rs0 Z −H = I m . By introducing A = A0 ZQ the covariance
matrix of the received signal becomes
Rx = AAH + Rn .
(4)
The only ambiguity left is the choice of the unitary matrix Q. In
order to choose Q, first the case of known noise covariance is
H
considered. Let R0 = A0 Rs0 AH
and assume that
0 = AA
Rn is known, then the EVD of the whitened covariance matrix
−1
−1
−1
−1
Rn 2 Rx Rn 2 − I p = Rn 2 R0 Rn 2 could be used to estimate
A. Consider the following eigenvalue problem
−1
−1
Rn 2 R0 Rn 2 U = U Γ,
(5)
1
−2
where Γ is a diagonal matrix. If we require U = Rn A then (5)
becomes
−1
−1
−1
−1
Rn 2 R0 Rn 2 Rn 2 A = Rn 2 A(AH R−1
n A)
−1
= Rn 2 AΓ.
(6)
One way to make sure that (6) holds, is by setting
AH R−1
n A = Γ.
(7)
The matrix Q is now chosen in such a way that (7) holds. With the
choice of Q there are no ambiguities left.
Finally we assume that the noise contributions are uncorrelated
so that
2. DATA MODEL
For a system with p receiving elements that is exposed to m sources,
a commonly used narrow-band model has the form
x(t) = A0 s0 (t) + n(t).
978-1-4673-1071-0/12/$31.00 ©2012 IEEE
(1)
477
Rx = AAH + D.
(8)
where D is a diagonal matrix.
For the remainder of this paper we refer to (8) as the (classical)
FA model.
2.1. Extension to Non-diagonal Covariance Matrices
3.1. Maximum Likelihood Estimator
The classical FA model is an especial case of a more general model
where the noise covariance could be modeled as Rn = M ⊙ Rn
where M is a symmetric matrix containing only zeros and ones. The
FA model then becomes
The aim is to find A and D that maximize the complex loglikelihood function
i
h
l(x; A, D) = N −log|π p | + log|R−1 | − tr(R−1 R̂) . (12)
Rx = AAH + M ⊙ Rn .
(9)
We assume that M = I p + B where B is a symmetric matrix with
zeros on its diagonal. If B = 0 then the classical model is obtained.
The choice of this mask depends on the application.
In the follwoing sections we first extend the estimation of the
classical FA to complex case and give an algorithm to estimate the
model parameters as given by (9).
3. PARAMETER ESTIMATION TECHNIQUES FOR
COMPLEX-VALUED DATA
The FA model for real-valued data, is a mature subject and various techniques for the estimation of the model parameters has been
suggested in literature[7, 8, 9]. Some of these algorithms extend to
complex case very naturally and some need closer look.
Before discussing the various algorithms, the problem definition for FA is examined in more detail. Given N samples from the
received signal x, we want to find  and D̂ based on the sample
covariance matrix
R̂x =
1
N
N
−1
X
x[i]x[i]H .
(10)
i=0
This problem becomes more complicated if the the number of
sources m is not known in advance. In this situation an estimate,
m̂, must also be chosen or found. It is also of interest to know the
maximum number of sources for which the FA model has any solution at all. In order to find this, we see how many free parameters
this model leave us for a given number of p and m. For a complex
sample covariance matrix there are p2 number of parameters. The
FA model parameters, A and D have 2pm and p free parameters
respectively, from which m2 are fixed by (7). This gives the total of
free parameters
s = p2 − 2pm + m2 − p
= (p − m)2 − p.
(11)
Even though the number of free parameters, s, is different for real
and complex case, the interpretation is the same. In [10] the real
case is discussed. In both cases if s < 0 there is an infinite number
of exact solutions and the problem is ill-posed. If s = 0 there is
one unique solution; this is for example in the case of EVD where
m = p and D is known or neglected (s becomes (p − m)2 = 0).
The last case is when s > 0, in this case it is not necessary that the
solution is exact and the parameters are estimated in such a way to
optimize a cost function.
In this paper we are interested in the case where s > 0. This the
√
case where m < ⌊p − p⌋ and D is unknown. We also assume that
the problem up to the matrix Q is (locally) identifiable. Conditions
for this type of identifiability are studied by [11, 12]. In the following
sections the maximum likelihood (ML) estimation of FA model is
discussed.
478
To achieve this we find the Fisher score and set it equal to zero. The
Fisher score for a proper Gaussian distributed signal is given by [13,
p.165]
∂
l(x; θ)
∂θj∗
"
"
#
„
«H #
«H
„
∂R
∂R
−1
−1
−1
= −N tr R
+ N tr R
R R̂ ,
∂θj
∂θj
(13)
t θj =
where the partial derivatives are Wirtinger derivatives; θj is the j–
th component of the vector θ = [a T1 , . . . , aTm , d]T where ak is the
k–th column of A and d = vectdiag(D) is a vector containing diagonal elements of D; θ∗ is the complex conjugate of θ. We have
either θj = aik or θj = di where i = 1, .., p and k = 1, .., m, so
∂R
∂R
and ∂d
.
we need to find the partial Wirtinger derivatives ∂a
i
ik
Here we give the final results of the derivations
∂R
= ei aH
k .
∂aik
(14)
and
∂R
= ei eH
(15)
i .
∂di
where ei is a unit vector with entry i equal to 1. The final results for
Fisher score in the matrix form become
“
”
T A = N −R−1 A + R−1 R̂R−1 A .
(16)
and
T D = N diag(−R−1 + R−1 R̂R−1 ).
(17)
Thus the Fisher score for the FA model is then
tθ = [vect(TA )T , vectdiag(TD )T ]T .
(18)
In [14] the same expression for the real-valued data is derived.
Even though no closed form solution for  and D̂ could be
found using (16), (17) or (18), they could be used to derive some interesting properties of the MLE for FA. For a discussion and derivation of these properties the reader is referred to [14, 10].
In the following section we will derive the necessary iterations
to approximate the MLE numerically.
3.1.1. Steep
For a function f (Z, Z ∗ ) the direction of the maximum change with
respect to Z is give by
∂
f (Z, Z ∗ )
∇Z f (Z, Z ∗ ) =
∂Z ∗
(19)
where the notation is adapted from [15].
Using (19), (13), (16) and (17) we have
and
∇A l(x; A, D) = T A
(20)
∇D l(x; A, D) = T D .
(21)
Âk+1 = Âk + µk Φk Âk
(22)
The iteration steps for the steep become
Attenuation
0
and
H
Âk Âk
(23)
−1
−1
Φk = −R−1
k + R k R̂R k
(24)
and Rk =
+ D̂ k .
The matrix Rk needs to be inverted at each iteration. This could
be done efficiently using Woodbury matrix identity
−1
−1
H
−1
−1
−1
R−1
Âk D̂ k
k = D̂ k − D̂ k  k (I m +  k D̂ k  k )
(25)
which requires the inversion of one diagonal matrix and one m × m
matrix. If the matrix Q is also calculated at each iteration then only
the inversion of two diagonal matrices is needed.
3.1.2. Steep for Extended FA
For the case of extended FA we need to compute the number of
free parameters again. In a system with p receiving elements and
m sources satisfying (7) we have
s = (p − m)2 − p − 2k,
(26)
where k is the number of off-diagonal elements. It is worth noting
that the covariance matrix is Hermitian and we only need to estimate
the elements below (or above) the diagonal. The factor 2k should not
be confused with the number of off-diagonal elements of M , which
is also 2k, the factor 2 comes from having complex entries on the offdiagonal elements of noise covariance matrix. Again we consider the
case where s > 0. This limits the number of off-diagonal elements
that could be estimated for a given number of sources and vice versa.
It is then straightforward to show that we only need to change (23)
to adapt the steep algorithm for extended FA.
Following the same procedure used to derive steep in previous
section we have
We have mji
∂R
= mji ej eH
i .
∂(rn )ij
= mij and so we have in the matrix form
(27)
∇R n ,R ∗ n l(x, A, Rn ) = N M ⊙ (−R−1 + R−1 R̂R−1 ). (28)
The iteration update for the extended FA model could now be written
as
R̂n k+1 = R̂n k + µk (M ⊙ Φk ).
(29)
H
where Φk is given by (24) and Rk = Âk Âk + R̂n k .
This concludes the estimation techniques for both classical and
extended FA. In the following section the CRB for the classical case
is presented.
4. CRAMÉR–RAO BOUND
The Cramér–Rao Bound is the lower bound on the covariance matrix
of the estimated parameters. As explained in order to have a unique
solution for the FA model the matrix Q needs to be fixed. Choosing
this matrix puts constraints on the parameter A. As a result the constrained CRB for complex parameters is used here. Following [16]
we define
1
0
vect(A)
∗
θ = @ vect(A ) A
vectdiag(D)
(30)
as augmented θ.
479
−5
Attenuation[dB]
where
D̂ k+1 = D̂ k + µk (I p ⊙ Φk )
FA
−10
EVD Uknown Noise
EVD Known Noise
−15
−20
−25
−30 2
10
p=10
m=2
3
10
4
10
5
10
6
10
N[# Samples]
Fig. 1. Spatial Filter attenuation
The augmented CRB for the estimated complex parameters with
the constraint k(θ) = 0 is
“
”−1
C(θ) ≥ U U H F U
UH,
(31)
where F is the augmented unconstrained complex Fisher information defined in [17] and U is a unitary basis for the null–space of K
given by
∂k T
K=
.
(32)
∂θ
Vectorization of (7) gives m2 equations needed to construct k.
For the proper Gaussian distribution we can use Bang’s formula
to find the Fisher information
F = J H (R−T ⊗ R−1 )J
(33)
where
∂vect(R)
J=
T
–
» ∂θ
∂vect(R)
∂vect(R)
∂vect(R)
,
,
=
∂vectT (A) ∂vectT (A∗ ) ∂vectdiagT (D)
= [(A∗ ⊗ I), (I ⊗ A)K, (I ◦ I)],
(34)
the matrix K is a permutation matrix such that Kvect(A) =
vect(AT ), ⊗, ◦ and ⊙ represent Kronecker, Khatri–Rao and
Hadamard product respectively. Using the tools developed here
it could be shown that the performance of the steep and scoring
method reaches CRB for the large number of samples.
5. SIMULATION RESULTS
We simulate a scenario in which the estimated subspace is used to
filter the incoming signals with the help of orthogonal projections.
The noise matrix is generated randomly in such a way that on each
receiving element the SNR is between 0 to −10 dB. We assume to
know the number of sources in advance. For this case there are two
sources, m = 2 and there are ten receiving elements, p = 10. We
use EVD without knowing the noise matrix, the FA (also without
knowing the noise) and EVD with the full knowledge of the noise
covariance matrix (whitening is used) and estimate the subspace for
different number of samples. The attenuation for each estimation is
found using
‚
‚
‚
‚
‚P̂ AAH P̂ ‚
‚ F
E= ‚
(35)
‚AAH ‚
F
“ H ”−1 H
where P̂ = Â Â Â
 .
Fig.1 is the average of 30 MC runs for the estimated attenuations. As expected FA estimates the subspace without knowing the
noise covariance very close to EVD method with the full knowledge
of the noise. The FA has slightly less attenuation than whitened
EVD but that could be expected given the fact that more parameters
are estimated.
1
1
0.8
0.8
0.9
0.6
0.8
0.4
0.7
0.2
0.6
0
0.5
−0.2
0.4
−0.4
0.3
−0.6
0.2
0.95
0.6
0.4
0.9
0.2
0.85
0
−0.2
0.8
−0.4
0.75
−0.6
−0.8
−0.8
0.1
0.7
0.8
0.6
0.4
0.2
0
−0.2 −0.4 −0.6 −0.8
0.8
East ← m → West
0.6
0.4
0.2
0
−0.2 −0.4 −0.6 −0.8
East ← m → West
Fig. 2. DFT Imaging withought FA
Fig. 3. DFT Imaging with Extended FA
6. EXPERIMENTAL RESULTS
[5] A.-J. Boonstra and A.-J. van der Veen, “Gain calibration methods for radio telescope arrays,” IEEE Tr. Signal Processing,
vol. 51, pp. 25–38, 2003.
The results presented here illustrate one of the applications of the extended FA model in radio astronomy. We follow discussion in [18,
pp.35-40] very closely. In fact the data set used here is the same
data set used in [18] from the LOFAR project. The total sky image is made using classical DFT method by combining data from
24 156kHz sub–bands distributed between 45.3 and 67.3 MHz and
10 seconds of integration per channel. Fig.2 shows the image without being pre–processed with the extended FA. There are two strong
sources, Cas A and Cyg A, and there is also a cloud–like radiation
from the extended emission in the sky. The extended emission affects the short–baselines that are smaller than 4 wavelengths. This
is exploited for modeling this extended emission as noise [18]. The
four wavelength criteria was used to create a mask matrix M 4λ and
the subspace of the two strong sources was estimated using extended
FA model. Fig.3 shows the image made using the DFT imaging on
R̂0 = ÂÂH . The estimated subspace gives an accurate estimation
for the sources.
7. CONCLUSIONS
It has been shown that the complex factor analysis model is the same
model which is commonly used in literature for narrow-band array
processing. The CRB and the Fisher score for the case of proper
complex Gaussian distribution are found using Wirtinger derivatives.
Some of the techniques used in factor analysis for real numbers are
extended to the case of the complex numbers. With the help of simulations and experimental data the potential use of FA as a generic
signal processing tool has been demonstrated.
[6] S. M. Kay, Fundamentals of Statistical Signal Processing, Estimation theory, vol. Volume I. Prentice Hall, 1993.
[7] K. G. Jöreskog, “A general approach to confirmatory maximum
likelihood factor analysis,” PSYCHOMETRIKA, vol. 34, no. 2,
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[8] S. Y. Lee, “The gauss-newton algorithm for the weighted least
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[9] A.-K. Seghouane, “An iterative projections algorithm for ml
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[10] K. Mardia, J. Kent, and J. Bibby, Multivariate Analysis. ACADEMIC PRESS, 1997.
[11] T. W. Anderson and H. Rubin, “Statistical inference in factor
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[12] A. Shapiro, “Identifiability of factor analysis: some results and
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8. REFERENCES
[1] C. Spearman, “The proof and measurement of association
between two things,” The American Journal of Psychology,
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[2] D. J. Bartholomew, M. Knott, and I. Moustaki, Latent Variable
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and Sons, 2011.
[3] R. A. Reyment and K. G. Jöreskog, Applied Factor Analysis in
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[4] S. Schell and W. Gardner, “Maximum likelihood and common
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[16] A. K. Jagannatham and B. D. Rao, “Cramer-rao lower bound
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[17] E. Ollila, V. Koivunen, and J. Eriksson, “On the Cramér-rao
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[18] S. J. Wijnsholds, Fish-Eye Observing with Phased Array Radio
Telescope. PhD thesis, Delft University of Technology, 2010.
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