dan@0: % distribution derivative method: 
dan@0: % 3rd degree system solver is used, make sure the data provided corresponds to that
dan@0: function dg3 = gen_ddm_3(krnls, krlns_ders, mf_ders, sig, N)
dan@0: % multi-frequency distribution derivative based non-stationary sinusoidal estimator 
dan@0: %  can estimate only 1 sinusoid at the once
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 x K, where R is the number of 
dan@0: %      non-static parameters to estimate and at the same time, the number 
dan@0: %      of kernels for each sinusoid, K
dan@0: %
dan@0: % krlns_ders: matrix of all the kernel time derivatives... N x R x K , 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: % K: number of sinusoids to estimate - NOT OVERLAPPING!!!
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: % 3rd degree system solver is used, make sure the data provided corresponds
dan@0: % to that
dan@0: R = size(krnls,2);
dan@0: assert(R == 3);
dan@0: assert(R == size(krlns_ders,2) );
dan@0: assert(R == size(mf_ders,2) );
dan@0: 
dan@0: [A, b] = ddm_lin_sys(krnls, krlns_ders, mf_ders, sig, N); % generate the linear system of eqs (slow)
dan@0: dg2 = lin_solve_dgr_3(A,b,1); %hardcoded degree 2 solver (fast)
dan@0: 
dan@0: