Reverse Levinson-Durbin recursion
r = rlevinson(a,efinal)
[r,u] = rlevinson(a,efinal)
[r,u,k] = rlevinson(a,efinal)
[r,u,k,e] = rlevinson(a,efinal)
The reverse Levinson-Durbin recursion implements the step-down algorithm for solving the following symmetric Toeplitz system of linear equations for r, where r = [r(1) … r(p + 1)] and r(i)* denotes the complex conjugate of r(i).
r = rlevinson(a,efinal) solves
the above system of equations for r given vector a,
where a = [1 a(2) … a(p + 1)]. In linear prediction applications,
the autocorrelation sequence of the input to the prediction error
filter, where r(1) is
the zero-lag element. The figure below shows the typical filter of
this type, where H(z) is
the optimal linear predictor, x(n)
is the input signal, is the predicted
signal, and e(n) is the prediction
Input vector a represents the polynomial coefficients of this prediction error filter in descending powers of z.
The filter must be minimum-phase to generate a valid autocorrelation
efinal is the scalar prediction error
power, which is equal to the variance of the prediction error signal, σ2(e).
[r,u] = rlevinson(a,efinal) returns
upper triangular matrix U from the UDU* decomposition
and E is a diagonal matrix with elements
returned in output
e (see below). This decomposition
permits the efficient evaluation of the inverse of the autocorrelation
u contains the prediction filter
a, from each iteration of the reverse
where ai(j) is the jth coefficient of the ith order prediction filter polynomial (i.e., step i in the recursion). For example, the 5th order prediction filter polynomial is
a5 = u(5:-1:1,5)'
u(p+1:-1:1,p+1)' is the input polynomial
[r,u,k] = rlevinson(a,efinal)
returns a vector
k of length p + 1 containing the reflection coefficients.
The reflection coefficients are the conjugates of the values in the
first row of
k = conj(u(1,2:end))
[r,u,k,e] = rlevinson(a,efinal) returns
a vector of length p + 1
containing the prediction errors from each iteration of the reverse
e(1) is the prediction
error from the first-order model,
e(2) is the prediction
error from the second-order model, and so on.
These prediction error values form the diagonal of the matrix E in the UDU* decomposition of R−1.
Estimate the spectrum of two sine waves in noise using an autoregressive model. Choose the best model order from a group of models returned by the reverse Levinson-Durbin recursion.
Generate the signal. Specify a sample rate of 1 kHz and a signal duration of 50 seconds. The sinusoids have frequencies of 50 Hz and 55 Hz. The noise has a variance of 0.2².
Fs = 1000; t = (0:50e3-1)'/Fs; x = sin(2*pi*50*t) + sin(2*pi*55*t) + 0.2*randn(50e3,1);
Estimate the autoregressive model parameters.
[a,e] = arcov(x,100); [r,u,k] = rlevinson(a,e);
Estimate the power spectral density for orders 1, 5, 25, 50, and 100.
N = [1 5 25 50 100]; nFFT = 8096; P = zeros(nFFT,5); for idx = 1:numel(N) order = N(idx); ARtest = flipud(u(:,order)); P(:,idx) = 1./abs(fft(ARtest,nFFT)).^2; end
Plot the PSD estimates.
F = (0:1/nFFT:1/2-1/nFFT)*Fs; plot(F, 10*log10(P(1:length(P)/2,:))) grid legend([repmat('Order = ',[5 1]) num2str(N')]) xlabel('Frequency (Hz)') ylabel('dB') xlim([35 70])
 Kay, Steven M. Modern Spectral Estimation: Theory and Application. Englewood Cliffs, NJ: Prentice-Hall, 1988.