% setting up filter parameters and the svd of the inputtostate response thetamid=1.325; [A,B]=cjordan(5,0.88*exp(thetamid*1i)); % obtaining state statistics R=dlsim_complex(A,B,y'); sv=Rsigma(A,B,th); plot(th(:),sv(:));
Inputtostate filter and state covarianceThe first step to explain the high resolution spectral analysis tools is to consider the inputtostate filter below and the corresponding the state statistics. The process is the input and is the state. Then the filter transfer function is A positive semidefinite matrix is the state covariance of the above filter, i.e. for some stationary input process , if and only if it satisfies the following equation for some rowvector . Starting from a finite number of samples, we denote the sample state covariance matrix. If the matrix is the companion matrix the filter is a tapped delay line: The corresponding state covariance is Toeplitz matrix. Thus the statecovariance formalism subsumes the theory of Toeplitz matrices. The inputtostate filter works as a “magnifying glass” or, as type of bandpass filter, amplifying the harmonics in a particular frequency interval. Shaping of the filter is accomplished via selection of the eigenvalues of . In the above example, we set eigenvalues of at , and the phase angle is the middle of the interval where resolution is desirable. Then we build the pair using routine cjordan.m, (and rjordan.m for real valued problem). The pair is normalized to satisfy The routine dlsim_complex.m generate the state covariance matrix (dlsim_real.m for real valued problem). The following figure plots versus . The gain at equal and is approximately dB below peak value. The window is marked in the following figure. The figure to the right shows the detail within the window of interest. % setting up filter parameters and the svd of the inputtostate response thetamid=1.325; [A,B]=cjordan(5,0.88*exp(thetamid*1i)); % obtaining state statistics R=dlsim_complex(A,B,y'); sv=Rsigma(A,B,th); plot(th(:),sv(:));
