Moving-average decomposition

In the Carathacute{e}odory-Fejacute{e}r-Pisarenko subspace methods, a Toeplitz structured covariance matrix T is decomposed as

 T=T_{rm {signal}}+ cI,

where T_{rm {signal}} is a singular covariance matrix attributed to the spectral lines and cI a covariance attributed to white noise. The estimation of spectral lines is then based on T_{rm signal}. A possible generalization of this idea is to allow the noise component to have a moving-average (MA) spectrum [1] . Thus, we decompose T as

 T = T_{rm {signal}}+T_{rm{ma}},

where T_{rm{ma}} is a covariance matrix corresponding to a MA process. Let A be a companion matrix of size mtimes m and

 begin{array}{rl} B:=&[1,; 0,; ldots,; 0]^prime, C:=&[c(1),;c(2), ldots, c(m)]. end{array}


 T_{rm{ma}}=left[           begin{array}{ccccc}             c(0) & ldots & c(m) & 0& ldots              vdots  & ddots &  & ddots &ddots              c(m)^* & ddots& &  &               0 & ddots &   &       &              vdots &ddots &  &  &             end{array}         right],

is the covariance matrix of a MA process if and only if there exist a Pgeq 0 such that

 left[ begin{array}{cc} P-A^*PA &  C^*-A^*PB C-B^*PA & c(0)-B^*PB end{array} right]geq0. hspace*{3cm} (a)

This is a consequence of the KYP positive-real lemma [1]. Assuming that m is the order of the MA process, it is natural to subtract the maximum amount of noise T_{rm{ma}} from T. Thus we are called to solve

 max left{c(0) ~mid~  Pgeq 0,  (a) rm{~holds}, T-T_{rm{ma}}geq0  right}.

This decomposition is obtained using routine MA_decomp.m which in turn uses the convex optimization package cvx.

An alternative approach is proposed in [2]. Let [q(0), ldots, q(m)] be the vector of the coefficients of a mth order MA filter, and let

 {c(-m), ldots, c(0),ldots, c(m) }

be the corresponding covariance sequence of the output of the filter driven by white noise with unit variance. Then

 c(k)= {rm trace}_kleft(left[                           begin{array}{c}                             q(0)                              vdots                              q(m)                            end{array}                         right][q(0)^*, ldots, q(m)^*] right), ~k=-m, ldots, 0,ldots, m,

where {rm trace}_k(cdot) denotes the sum of the kth sub-diagonal of a matrix. This was relaxed to the constraint used in [2] as follows

 begin{array}{rl} Qgeq&0, c(k)=&{rm trace}_k(Q), k=-m, ldots,0,ldots, m. end{array}


Consider the following sequence

 y_{k}=a_1 e^{itheta_1k+phi_1}+a_2 e^{itheta_2k+phi_2}+rm{noise}_k, rm{~for~} k=0,ldots,{N-1},

with theta_1=1.3 and theta_2=1.35, N=100, and the noise component {rm{noise}_k} generated by a MA filter driven by white noise (complex with independent real and imaginary parts). The MA filter is 3rd with zeros at 0.85, 0.8e^{i2.5}, and 0.8e^{-i2.5} respectively.

%  SIGNAL = sinusoids + noise
%  Setting up the signal parameters and time history
N=100; n=15;
a0=1; a1=1.5; theta1=1.3; a2=2; theta2=1.35;
k=1:N; k=k(:);
% MA coefficient vector
b=[1.0000    0.4318   -0.4496   -0.5440];
% generate noise
NN=2048; th=linspace(0,2*pi,NN);
[Ts, Tma]=MA_decomp(T,3);
        plot(theta1,a1,'r^','MarkerSize',12);hold on;
        legend('true spectral line','true MA spectrum','Location','North');
        arrow([theta1 theta2],[a1 a2],12);
        set(gca,'xlim',[0 2*pi]);

r_ma=[fliplr(Tma(1,:)) Tma(1,2:end)];
r_s=r_s(w_s<pi); w_s=w_s(w_s<pi);
        plot(w_s(1),r_s(1),'r^','MarkerSize',12);hold on;
        legend('estimated spectral line','estimated MA spectrum','Location','North');
        set(gca,'xlim',[0 2*pi]);

[Ts0, Tn]=MA_decomp(T,0);
r_s0=r_s0(w_s0<pi); w_s0=w_s0(w_s0<pi);
        plot(w_s0(1),r_s0(1),'r^','MarkerSize',12);hold on;
        legend('estimated spectral line','estimated noise variance','Location','North');
        set(gca,'xlim',[0 2*pi]);

The first figure below shows the true spectral lines marked with arrows superimposed on the MA spectrum. The figure in the middle shows the estimated spectral lines and the estimated 3rd order MA spectrum. There are two dominant spectral lines locate very closely to the true values. The figure at the bottom shows the estimate when the noise component is taken to be white. The blue line shows the noise variance. In this case, only one dominant spectral line is detected near the true ones.



[1] T.T. Georgiou, “Decomposition of Toeplitz matrices via convex optimization,” IEEE Signal Processing Letters, 13(9): 537-540, September 2006.

[2] P. Stoica, L. Du, J. Li and T. Georgiou, “A new method for moving-average parameter estimation”, Signals, Systems and Computers (ASILOMAR), 2010 Conference Record of the Forty Fourth Asilomar Conference on, 1817–1820, 2010.