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作 者:张银兵[1] 赵俊渭[1] 郭业才[2] 李金明[1]
机构地区:[1]西北工业大学航海学院,陕西西安710072 [2]南京信息工程大学电子与信息工程学院,江苏南京210044
出 处:《西北工业大学学报》2010年第2期202-206,共5页Journal of Northwestern Polytechnical University
基 金:全国优秀博士学位论文作者专项基金(200753)资助
摘 要:为了改善α稳定噪声环境中盲均衡器的性能,文章提出一种改进常数模盲均衡算法(MAEC-CMA)。对均衡器输入信号进行软限幅,并对原自适应误差受限常数模盲均衡算法(AECCMA)的误差信号进行非线性变换,有效地抑制了α稳定噪声的影响。采用2种水声信道,在高斯噪声与α稳定噪声的情况下对算法进行了计算机仿真。结果表明:在高斯噪声环境中,MAECCMA算法与AECCMA算法具有相似的性能,相对于常数模(CMA)算法和归一化最小平均绝对偏差(NLMAD)算法它具有较快的收敛速度;在α稳定噪声环境中,文中提出的MAECCMA算法性能优于其它3种算法。Aim.The AECCMA algorithm proposed by S.Choi et al in Ref.5 is better in convergence than CMA algorithm in Gaussian noise.We now propose a modified AECCMA(MAECCMA) algorithm that can also suppress α-stable noise while retaining its good convergence in Gaussian noise.Section 1 of the full paper briefs α-stable noise.Section 2 briefs the normalized least mean absolute deviation(NLMAD) algorithm.Section 3 explains our MAECCMA algorithm;it discusses how to modify the AECCMA algorithm and then presents the details of modification in two steps:(1) adding to the software the capability of limiting the amplitude of the equalizer input,(2) transforming nonlinearly the error signals of the AECCMA algorithm to suppress the α-stable noise.Section 4 presents the computer simulation of our MAECCMA algorithm respectively in Gaussian noise and in α-stable noise,using two underwater acoustic channels.The simulation results,given in Figs.1 through 4,show preliminarily that:(1) the performance of our MAECCMA algorithm is almost the same as that of AECCMA algorithm in Gaussian noise;(2) both algorithms have faster convergence speed than the CMA algorithm and the NLMAD algorithm in Gaussian noise;(3) the performance of our MAECCMA algorithm is the best among the above four algorithms in α-stable noise;thus it can steadily suppress the residual inter-symbol interference caused by the multi-path effect of an underwater acoustic channel.
分 类 号:TN911.5[电子电信—通信与信息系统]
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