dan@0: % a high-level function for computing a DTFT based DDM 
dan@0: % (constructs the linear system of equations using distribution derivative rule and solves it via 
dan@0: % pseudo-inverse (pinv). Use this function if non-Fourier kernels are needed or
dan@0: % DTFT at specific frequency rather than FFT is required for whatever reason...
dan@0: % no zero-padding is used internally
dan@0: function [gdi,r, A,b, gdi_lns, r_lns] = ddm_gen(krnls, krlns_ders, mf_ders, sig, N, tol)
dan@0: %generic multi-frequency distribution derivative based estimator for
dan@0: % non-stationary sinusoidal analysis
dan@0: %
dan@0: %
dan@0: % [1] Michael Betser: Sinusoidal Polynomial Estimation Using The Distribution
dan@0: % Derivative, in IEEE Transactions on Signal Processing, Vol.57, Nr. 12,
dan@0: % December 2009
dan@0: %
dan@0: % krnls: matrix of all the kernels... N x R , where R is the number of 
dan@0: %      non-static parameters to estimate and at the same time, the number 
dan@0: %      of kernels
dan@0: %
dan@0: % krlns_ders: matrix of all the kernel time derivatives... N x R , where R 
dan@0: %     is the number of non-static parameters to estimate and at the same 
dan@0: %     time, the number of kernels
dan@0: %
dan@0: % mf_ders: matrix of all the model function time derivatives... N x Q , where Q 
dan@0: %     is the number of model functions
dan@0: %
dan@0: %
dan@0: % sig: vector - signal, N x 1 (CAUTION: MUST be column vector!!!)
dan@0: %
dan@0: % N: odd integer - signal buffer length, ...
dan@0: %
dan@0: % For any reasonable use, Q equals R, otherwise it makes little sense.
dan@0: % Kernels must include the window function...
dan@0: 
dan@0: [A,b] = ddm_lin_sys(krnls, krlns_ders, mf_ders, sig, N);
dan@0: 
dan@0: % solving via pinv with provided tolerance
dan@0: gdi = pinv(A,tol) * b;
dan@0: % r = rcond(A);
dan@0: % [gdi_lns r_lns]  = linsolve(A,b);
dan@0: end