# Table 4 The D-CMLF algorithm

 Stage inputs $$\gamma ^{f}_{\ell -1,1}(n)=\gamma _{\ell -1}(n-1), \gamma ^{b}_{\ell -1,1}(n)=\gamma _{\ell -1}(n)$$ (T.4.1) $$r^{b}_{\ell -1,k}(-1)=r^{f}_{\ell -1,k}(-1)=\delta, (k=1,\ldots,M)$$ (T.4.2) $$\Delta ^{e}_{\ell,k}(-1)=\Delta ^{f}_{\ell,k}(-1)=\Delta ^{b}_{\ell,k}(-1)= \Delta ^{eq}_{\ell }(0)=0.0, (k=1,\ldots,M)$$ (T.4.3) For k = 1,…, M Computations at SOPs $$r^{b}_{\ell -1,k}(n) = \lambda _{k} \ r^{b}_{\ell -1,k}(n-1) + \gamma ^{b}_{\ell -1,k}(n) \ \mid b_{\ell -1}^{k}(n) \mid ^{2}$$ (T.4.4) $$\gamma ^{b}_{\ell -1,k+1}(n)=\gamma ^{b}_{\ell -1,k}(n) - \mid \gamma ^{b}_{\ell -1,k}(n) \mid ^{2} \mid b_{\ell -1}^{k}(n)\mid ^{2}/r^{b}_{\ell -1,k}(n)$$ (T.4.5) $$r^{f}_{\ell -1,k}(n) = \lambda _{k} \ r^{f}_{\ell -1,k}(n-1) + \gamma ^{f}_{\ell -1,k}(n) \ \mid f_{\ell -1}^{k}(n) \mid ^{2}$$ (T.4.6) $$\gamma ^{f}_{\ell -1,k+1}(n)=\gamma ^{f}_{\ell -1,k}(n) - \mid \gamma ^{f}_{\ell -1,k}(n) \mid ^{2} \mid f_{\ell -1}^{k}(n) \mid ^{2}/r^{f}_{\ell -1,k}(n)$$ (T.4.7) Joint process estimation (ROP) $$e_{\ell }^{k}(n)=e_{\ell -1}^{k}(n) - \Delta ^{e^{*}}_{\ell,k}(n-1) \ b_{\ell -1}^{k}(n)$$ (T.4.8) $$\Delta ^{e}_{\ell,k}(n)= \alpha (\Delta ^{e}_{\ell,k}(n-1) + \gamma ^{b}_{\ell -1,k}(n) e^{k^{\ast }}_{\ell }(n) b^{k}_{\ell -1}(n)/r^{b}_{\ell -1,k}(n)) + (1 - \alpha) \Delta ^{eq}_{\ell }(n-1)$$ (T.4.9) Combination processing (type-3 processor) $$e_{\ell }^{eq}(n) = e_{\ell }^{eq}(n) + v_{\ell }^{k}(n) \ e_{\ell }^{k}(n)$$ (T.4.10) $$\Delta ^{eq}_{\ell }(n) = \Delta ^{eq}_{\ell }(n) + v_{\ell }^{k}(n) \ \Delta ^{e}_{\ell,k}(n)$$ (T.4.11) $$a_{\ell }^{k}(n+1)=a_{\ell }^{k}(n)-\mu _{a} \ e^{eq}_{\ell }(n) \ (e_{\ell }^{k}(n) - e^{eq}_{\ell }(n)) \ v_{\ell }^{k}(n) + \rho \ (a^{k}(n)-a^{k}(n-1))$$ (T.4.12) $$\beta _{\ell }(n+1)= \beta _{\ell }(n+1) + e_{\ell }^{a^{k}(n+1)}$$ (T.4.13) $$v_{\ell }^{k}(n+1)= \frac {e^{a_{\ell }^{k}(n+1)}}{\beta _{\ell }(n+1)}$$ (T.4.14) Forward error prediction (ROP) $$f_{\ell }^{k}(n)=f_{\ell -1}^{k}(n) - \Delta ^{f^{*}}_{\ell,k}(n-1) \ b_{\ell -1}^{k}(n-1)$$ (T.4.15) $$\Delta ^{f}_{\ell,k}(n)= \Delta ^{f}_{\ell,k}(n-1) + \gamma ^{b}_{\ell -1,k}(n-1) \ f^{k^{\ast }}_{\ell }(n) b^{k}_{\ell -1}(n-1)/r^{b}_{\ell -1,k}(n-1)$$ (T.4.16) Backward error prediction (ROP) $$b^{k}_{\ell }(n)=b_{\ell -1}^{k}(n-1) - \Delta ^{b^{*}}_{\ell,k}(n-1) \ f_{\ell -1}^{k}(n)$$ (T.4.17) $$\Delta ^{b}_{\ell,k}(n)= \Delta ^{b}_{\ell,k}(n-1) + \gamma ^{f}_{\ell -1,k}(n) \ b^{k^{\ast }}_{\ell }(n) f^{k}_{\ell -1}(n)/r^{f}_{\ell -1,k}(n)$$ (T.4.18) End Stage outputs $$\gamma _{\ell }(n)=\gamma ^{b}_{\ell -1,M+1}(n)$$ (T.4.19)