A simplified FTF-type algorithm for adaptive filtering
Signal Processing
Computers and Electrical Engineering
SAR imaging via efficient implementations of sparse ML approaches
Signal Processing
Computers and Electrical Engineering
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The authors investigate the recently suggested fast Newton family for adaptive filtering in the context of acoustic echo cancellation, with emphasis on the mobile radio case. A distinctive advantage of the fast Newton transversal filter (FNTF) is that it can offer high performance with speech inputs at low computational cost. They discuss possible implementations and compare the FNTF with classical schemes in terms of complexity. A complete numerically stabilized version is presented, and additional features for proper real-time operation with speech are discussed. Experimental comparisons using various signals and real situations show that in all cases, the FNTF behaves similarly to the standard fast RLS transversal filter (FTF) algorithm, whereas its complexity is only slightly higher than that of the normalized LMS (NLMS). Compared with the NLMS, the experiments show that in the context investigated, the latter exhibits inferior performance with respect to convergence and tracking. Thus, they demonstrate that the FNTF is an efficient scheme for acoustic echo cancellation in mobile radio