共查询到19条相似文献,搜索用时 140 毫秒
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一种改进的变步长LMS自适应滤波算法 总被引:1,自引:0,他引:1
传统的固定步长的LMS算法难于同时获取较快的收敛速度与较小的稳态误差,基于这一矛盾,将变步长算法与变换域算法相结合,提出一种改进的LMS自适应算法以获得较快的收敛速度和较小的稳态误差。仿真结果表明,此算法在收敛速度与稳态误差的性能上均不同程度地优于其他同类算法,尤其是在低信噪比的情况下,其性能的优越性更为突出。 相似文献
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一种基于自适应阵列天线的波束赋形算法 总被引:1,自引:0,他引:1
自适应阵列天线中的数字波束赋形(DBF)技术是智能天线数字信号处理部分的核心.提出了一种可用于自适应阵列波束赋形的SMI-LMS算法--由SMI(采样协方差矩阵求逆)算法决定LMS(最小均方)算法的初始权向量.该算法充分结合了SMI算法收敛速度快和LMS算法稳态误差小的优点,能在较强干扰环境下,确保权向量的快速收敛和跟踪速度.与传统的LMS算法相比,SMI-LMS算法具有良好的收敛性能、较快的跟踪速度和较小的输出误差,并可以有效改善自适应方向图的副瓣性能.仿真结果验证了该结论. 相似文献
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波束形成技术已被广泛应用于通信领域,但传统的波束形成算法的收敛速度仍然有待于提高。文中旨在针对现有波束形成算法收敛速度慢和稳态误差较大的现状,提出了改进波束形成算法,旨在减小稳态误差的同时提高算法的收敛速度。通过理论分析和仿真表明,文中所提出的算法提升了收敛速度并降低了稳态误差。文中首先对所提出的改进算法进行理论分析,并在此基础上将所提出的算法构建在FPGA平台上,进一步论证了文中算法的可行性。 相似文献
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基于非线性盲源分离的维纳系统算法中,采用固定步长导致算法的收敛速度和稳态误差之间存在矛盾,直接影响分离算法的性能。为了解决该问题,提出了基于非线性函数的变步长维纳系统盲源分离方法。该方法将更新的步长以非线性函数的形式引入到分离算法中,使得稳态时参数更新的步长尽可能小,以避免发生振荡。变步长算法在分离过程中的每次更新都会使步长自动进行合理的调整,使得收敛速度提高了53%,误差减小了45%。实验仿真表明,相对原算法,提出的维纳系统盲源分离方法可以更好地分离出信源信号,而且具有较小的误差和较快的收敛速度。 相似文献
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In order to improve that the LMS algorithm for determining step size cannot satisfy both rapid convergence and low steady-state misalignment errors,a variable step size algorithm (IVSSLMS) based on an improved hyperbolic tangent function was proposed.The step size feedback factor and the correlation value of the error signal were used to adjust the step size,and the problems of slow convergence speed and poor anti-noise ability of the fixed-step size algorithm was overcame.The performance and parameters settings of the proposed algorithm were analyzed.The simulation results show that IVSSLMS has a faster convergence rate,lower steady-state error and better system tracking capability than the existing variable step size algorithms under high and low SNR conditions. 相似文献
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High-throughput satellites use multi-beam technology to achieve polarization isolation and spatial isolation to reuse frequency resources,resulting in increased traffic capacity,but this presents a higher demand for fast adaptive beamforming algorithms.In order to solve the shortcoming that the convergence rate and steady-state error can not be satisfied simultaneously when using the LMS algorithm,an improved least mean square algorithm was proposed,which used the statistical average gradient update to solve the problem of the formation of beam instability caused by the instantaneous gradient,which can speed up at the beginning of the beamforming and maintain a small error value after the convergence has reached a steady state.The use of 61-element hexagonal array phased array antenna to form a 7-point beam in an environment where high-throughput satellites have a strong rainfall attenuation was considered.The results show that the improved algorithm can greatly improve the convergence speed and obtain better steady state performance under the condition of only a small increase in complexity,which can be applied to high-throughput satellite beamforming technology with severe inter-beam co-channel interference in emergency communication scenarios. 相似文献
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通过分析NLMS[1]和VSSLMS[7]两种算法控制时变步长的思想及这两种算法的优缺点,文章提出了一种改进的归一化变步长算法,它同时使用输入信号积累和瞬时误差来控制步长更新.该算法的优越性在于收敛速度快,尤其在系统跳变时也能快速收敛,可很好地应用于自适应预测系统中.理论分析与计算机仿真结果都表明该算法收敛速度快、失调量小、稳定性好, 且在低信噪比的环境中比其他同类算法有更好的性能. 相似文献
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针对OFDM系统中传统信道估计算法在冲击噪声环境中性能急剧下降的问题,提出了一种基于韦伯分布函数的顽健型变步长符号算法进行信道估计。在深入研究冲击噪声特性及韦伯分布函数性质的基础上,提出了采用估计误差绝对值的韦伯分布函数控制步长的低复杂度变步长符号算法。该算法在利用传统符号算法顽健性的基础上,采用估计误差的韦伯分布函数动态地改变迭代符号算法的步长,从而能够以较低的复杂度提高变步长符号算法在冲击噪声环境中的收敛速度。算法复杂度分析及仿真结果表明,在冲击噪声环境下所提算法相较于传统自适应滤波信道估计算法能够以更低的复杂度、更快的收敛速度达到相同的信道估计均方误差。 相似文献
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变步长LMS自适应滤波算法通过构造合适的步长因子有效的解决了传统LMS算法收敛速度和稳态误差相矛盾的问题.变换域LMS自适应滤波算法通过正交变换降低了输入信号矩阵的相关性,提高了算法的收敛速度.将这两种算法相结合,提出了一种新的基于小波变换的变步长LMS自适应滤波算法.仿真结果表明,该算法无论是收敛速度还是稳态误差都有了很大的提高. 相似文献
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A fast learning algorithm for Gabor transformation 总被引:2,自引:0,他引:2
An adaptive learning approach for the computation of the coefficients of the generalized nonorthogonal 2-D Gabor (1946) transform representation is introduced. The algorithm uses a recursive least squares (RLS) type algorithm. The aim is to achieve minimum mean squared error for the reconstructed image from the set of the Gabor coefficients. The proposed RLS learning offers better accuracy and faster convergence behavior when compared with the least mean squares (LMS)-based algorithms. Applications of this scheme in image data reduction are also demonstrated. 相似文献