Interference signals due to scattering from surface and reflecting from bottom is one of the most important problems of reliable communications in shallow water channels. To solve this problem, one of the best suggest...Interference signals due to scattering from surface and reflecting from bottom is one of the most important problems of reliable communications in shallow water channels. To solve this problem, one of the best suggested ways is to use adaptive equalizers. Convergence rate and misadjustment error in adaptive algorithms play important roles in adaptive equalizer performance. In this paper, affine projection algorithm （APA）, selective regressor APA（SR-APA）, family of selective partial update （SPU） algorithms, family of set-membership （SM） algorithms and selective partial update selective regressor APA （SPU-SR-APA） are compared with conventional algorithms such as the least mean square （LMS） in underwater acoustic communications. We apply experimental data from the Strait of Hormuz for demonstrating the efficiency of the proposed methods over shallow water channel. We observe that the values of the steady-state mean square error （MSE） of SR-APA, SPU-APA0 SPU-normalized least mean square （SPU-NLMS）, SPU-SR-APA0 SM-APA and SM-NLMS algorithms decrease in comparison with the LMS algorithm. Also these algorithms have better convergence rates than LMS type algorithm.展开更多
A new fast adaptive filtering algorithm was presented by using the correlations between the signal’s former and latter sampling times.The proof of the new al-gorithm was also presented,which showed that its optimal w...A new fast adaptive filtering algorithm was presented by using the correlations between the signal’s former and latter sampling times.The proof of the new al-gorithm was also presented,which showed that its optimal weight vector was the solution of generalized Wiener equa-tion.The new algorithm was of simple structure,fast con-vergence,and less stable maladjustment.It can handle many signals including both uncorrelated signal and strong corre-lation signal.However,its computational complexity was comparable to that of the normalized least-mean-square(NLMS)algorithm.Simulation results show that for uncor-related signals,the stable maladjustment of the proposed algorithm is less than that of the VS-NLMS algorithm,and its convergence is comparable to that of the algorithm pro-posed in references but faster than that of L.E-LMS algo-rithm.For strong correlation signal,its performance is su-perior to those of the NLMS algorithm and DCR-LMS al-gorithm.展开更多
文摘Interference signals due to scattering from surface and reflecting from bottom is one of the most important problems of reliable communications in shallow water channels. To solve this problem, one of the best suggested ways is to use adaptive equalizers. Convergence rate and misadjustment error in adaptive algorithms play important roles in adaptive equalizer performance. In this paper, affine projection algorithm （APA）, selective regressor APA（SR-APA）, family of selective partial update （SPU） algorithms, family of set-membership （SM） algorithms and selective partial update selective regressor APA （SPU-SR-APA） are compared with conventional algorithms such as the least mean square （LMS） in underwater acoustic communications. We apply experimental data from the Strait of Hormuz for demonstrating the efficiency of the proposed methods over shallow water channel. We observe that the values of the steady-state mean square error （MSE） of SR-APA, SPU-APA0 SPU-normalized least mean square （SPU-NLMS）, SPU-SR-APA0 SM-APA and SM-NLMS algorithms decrease in comparison with the LMS algorithm. Also these algorithms have better convergence rates than LMS type algorithm.
文摘A new fast adaptive filtering algorithm was presented by using the correlations between the signal’s former and latter sampling times.The proof of the new al-gorithm was also presented,which showed that its optimal weight vector was the solution of generalized Wiener equa-tion.The new algorithm was of simple structure,fast con-vergence,and less stable maladjustment.It can handle many signals including both uncorrelated signal and strong corre-lation signal.However,its computational complexity was comparable to that of the normalized least-mean-square(NLMS)algorithm.Simulation results show that for uncor-related signals,the stable maladjustment of the proposed algorithm is less than that of the VS-NLMS algorithm,and its convergence is comparable to that of the algorithm pro-posed in references but faster than that of L.E-LMS algo-rithm.For strong correlation signal,its performance is su-perior to those of the NLMS algorithm and DCR-LMS al-gorithm.
