To meet the requirements of quick positioning of mobile terminals from base stations(BSs)or third-party devices,as well as to improve the convergence speed and reduce the steady state maladjustment of the least mean s...To meet the requirements of quick positioning of mobile terminals from base stations(BSs)or third-party devices,as well as to improve the convergence speed and reduce the steady state maladjustment of the least mean square(LMS)method,a new logarithmic-sigmoid variable step-size LMS(LG-SVSLMS)was proposed and applied to estimate the direction of arrival(DOA)of orthogonal frequency division multiple access(OFDMA)signals.Based on the proposed LG-SVSLMS,a non-blind DOA estimation system for OFDMA signals was constructed.The proposed LG-SVSLMS adopts a new multi-parameter step-size update function which combines the sigmoid function and the logarithmic function.It controls the adjustment magnitude of step-size during the initial and steady state phases of the LMS method to achieve both a high convergence speed and low steady state maladjustment.Finally,simulation was conducted to verify the performance of the LG-SVSLMS.The simulation results show that the non-blind DOA estimation system based on the LG-SVSLMS can accurately estimate the DOA of the target signal in the scenario where interference signals from multi-source and multi-path fading signals arrive at the third-party devices asynchronously with the target signal,and the estimation deviation is within±3°.The non-blind DOA estimation for OFDMA signals with the proposed LG-SVSLMS is of great significance for the instant positioning technology of mobile terminals based on the adaptive antenna array.展开更多
With independence assumption, this paper proposes and proves the superior step-size theorem on least mean square (LMS) algorithm, from the view of minimizing mean squared error (MSE). Following the theorem we construc...With independence assumption, this paper proposes and proves the superior step-size theorem on least mean square (LMS) algorithm, from the view of minimizing mean squared error (MSE). Following the theorem we construct a parallel variable step-size LMS filters algorithm. The theoretical model of the proposed algorithm is analyzed in detail. Simulations show the proposed theoretical model is quite close to the optimal variable step-size LMS (OVS-LMS) model. The experimental learning curves of the proposed algorithm also show the fastest convergence and fine tracking performance. The proposed algorithm is therefore a good realization of the OVS-LMS model.展开更多
To solve the contradiction between convergence rate and steady-state error in least mean square (LMS) algorithm, basing on independence assumption, this paper proposes and proves the optimal step-size theorem from the...To solve the contradiction between convergence rate and steady-state error in least mean square (LMS) algorithm, basing on independence assumption, this paper proposes and proves the optimal step-size theorem from the view of minimizing mean squared error (MSE). The theorem reveals the one-to-one mapping between the optimal step-size and MSE. Following the theorem, optimal variable step-size LMS (OVS-LMS) model, describing the theoretical bound of the convergence rate of LMS algorithm, is constructed. Then we discuss the selection of initial optimal step-size and updating of optimal step-size at the time of unknown system changing. At last an optimal step-size LMS algorithm is proposed and tested in various environments. Simulation results show the proposed algorithm is very close to the theoretical bound.展开更多
A new normalized least mean square(NLMS) adaptive filter is first derived from a cost function, which incorporates the conventional one of the NLMS with a minimum-disturbance(MD)constraint. A variable regularization f...A new normalized least mean square(NLMS) adaptive filter is first derived from a cost function, which incorporates the conventional one of the NLMS with a minimum-disturbance(MD)constraint. A variable regularization factor(RF) is then employed to control the contribution made by the MD constraint in the cost function. Analysis results show that the RF can be taken as a combination of the step size and regularization parameter in the conventional NLMS. This implies that these parameters can be jointly controlled by simply tuning the RF as the proposed algorithm does. It also demonstrates that the RF can accelerate the convergence rate of the proposed algorithm and its optimal value can be obtained by minimizing the squared noise-free posteriori error. A method for automatically determining the value of the RF is also presented, which is free of any prior knowledge of the noise. While simulation results verify the analytical ones, it is also illustrated that the performance of the proposed algorithm is superior to the state-of-art ones in both the steady-state misalignment and the convergence rate. A novel algorithm is proposed to solve some problems. Simulation results show the effectiveness of the proposed algorithm.展开更多
By analyzing algorithms available for variable step size least mean square(LMS)adaptive filter,a new modified LMS adaptive filtering algorithm with variable step size is proposed,along with performance analysis based ...By analyzing algorithms available for variable step size least mean square(LMS)adaptive filter,a new modified LMS adaptive filtering algorithm with variable step size is proposed,along with performance analysis based on different parameters.Compared with the existing algorithms through the simulation,the proposed algorithm has faster convergence speed and smaller steady state error.展开更多
The contradiction of variable step size least mean square(LMS)algorithm between fast convergence speed and small steady-state error has always existed.So,a new algorithm based on the combination of logarithmic and sym...The contradiction of variable step size least mean square(LMS)algorithm between fast convergence speed and small steady-state error has always existed.So,a new algorithm based on the combination of logarithmic and symbolic function and step size factor is proposed.It establishes a new updating method of step factor that is related to step factor and error signal.This work makes an analysis from 3 aspects:theoretical analysis,theoretical verification and specific experiments.The experimental results show that the proposed algorithm is superior to other variable step size algorithms in convergence speed and steady-state error.展开更多
This paper puts forward a new variable step size LMS adaptive algorithm based on variable region. The step size p（k） in the algorithm varies with the variation of the region of deviation e （k） to ensure the optimi...This paper puts forward a new variable step size LMS adaptive algorithm based on variable region. The step size p（k） in the algorithm varies with the variation of the region of deviation e （k） to ensure the optimization of the three performance objectives including initial convergent speed, trace ability of the time-varying system and steady disregulation. The paper demonstrates the convergence of the algorithm accompanied by random noise,展开更多
为了改进现有的变步长最小均方误差(least mean square,LMS)算法在低信噪比时性能较差的缺陷,提出了一种基于改进的双曲正切函数的变步长LMS算法,从理论分析和仿真实验两方面讨论了引入参数对算法收敛性、跟踪性、稳定性的影响及算法的...为了改进现有的变步长最小均方误差(least mean square,LMS)算法在低信噪比时性能较差的缺陷,提出了一种基于改进的双曲正切函数的变步长LMS算法,从理论分析和仿真实验两方面讨论了引入参数对算法收敛性、跟踪性、稳定性的影响及算法的抗干扰性。理论分析和仿真实验表明该算法在高低信噪比时均具有较快的收敛速度和跟踪速度以及较小的稳态误差和稳态失调,并且在低信噪比时该算法的收敛性、跟踪性、稳态性均优于其他多种变步长算法。展开更多
针对固定步长最小均方(LMS,least mean square)算法以及变步长LMS算法在收敛速度与稳态误差性能方面的不足,本文提出了一种新的基于对数函数改进的LMS算法.由于该算法中不涉及指数的运算,使得算法的计算量大大下降,收敛速度更快.仿真结...针对固定步长最小均方(LMS,least mean square)算法以及变步长LMS算法在收敛速度与稳态误差性能方面的不足,本文提出了一种新的基于对数函数改进的LMS算法.由于该算法中不涉及指数的运算,使得算法的计算量大大下降,收敛速度更快.仿真结果表明,对数函数改进的LMS算法比基于反正切函数改进的LMS算法具有近似相同的稳态误差性能,然而本文算法收敛更快,速度平均提高1.5倍.并且比基于双曲正切函数改进的LMS算法中的稳态误差平均降低0.5倍,同时收敛速度平均提高1.0倍.展开更多
基金The Social Development Projects of Jiangsu Science and Technology Department(No.BE2018704)the Technological Innovation Projects of Ministry of Public Security of China(No.20170001)。
文摘To meet the requirements of quick positioning of mobile terminals from base stations(BSs)or third-party devices,as well as to improve the convergence speed and reduce the steady state maladjustment of the least mean square(LMS)method,a new logarithmic-sigmoid variable step-size LMS(LG-SVSLMS)was proposed and applied to estimate the direction of arrival(DOA)of orthogonal frequency division multiple access(OFDMA)signals.Based on the proposed LG-SVSLMS,a non-blind DOA estimation system for OFDMA signals was constructed.The proposed LG-SVSLMS adopts a new multi-parameter step-size update function which combines the sigmoid function and the logarithmic function.It controls the adjustment magnitude of step-size during the initial and steady state phases of the LMS method to achieve both a high convergence speed and low steady state maladjustment.Finally,simulation was conducted to verify the performance of the LG-SVSLMS.The simulation results show that the non-blind DOA estimation system based on the LG-SVSLMS can accurately estimate the DOA of the target signal in the scenario where interference signals from multi-source and multi-path fading signals arrive at the third-party devices asynchronously with the target signal,and the estimation deviation is within±3°.The non-blind DOA estimation for OFDMA signals with the proposed LG-SVSLMS is of great significance for the instant positioning technology of mobile terminals based on the adaptive antenna array.
文摘With independence assumption, this paper proposes and proves the superior step-size theorem on least mean square (LMS) algorithm, from the view of minimizing mean squared error (MSE). Following the theorem we construct a parallel variable step-size LMS filters algorithm. The theoretical model of the proposed algorithm is analyzed in detail. Simulations show the proposed theoretical model is quite close to the optimal variable step-size LMS (OVS-LMS) model. The experimental learning curves of the proposed algorithm also show the fastest convergence and fine tracking performance. The proposed algorithm is therefore a good realization of the OVS-LMS model.
基金This work was supported in part by the National Fundamental Research Program(Grant No.G1998030406)the National Natural Science Foundation of China(Grant No.69972020)by the State Key Lab on Microwave and Digital Communications,Department of Electronics Engineering,Tsinghua University.
文摘To solve the contradiction between convergence rate and steady-state error in least mean square (LMS) algorithm, basing on independence assumption, this paper proposes and proves the optimal step-size theorem from the view of minimizing mean squared error (MSE). The theorem reveals the one-to-one mapping between the optimal step-size and MSE. Following the theorem, optimal variable step-size LMS (OVS-LMS) model, describing the theoretical bound of the convergence rate of LMS algorithm, is constructed. Then we discuss the selection of initial optimal step-size and updating of optimal step-size at the time of unknown system changing. At last an optimal step-size LMS algorithm is proposed and tested in various environments. Simulation results show the proposed algorithm is very close to the theoretical bound.
基金supported by the National Natural Science Foundation of China(61571131 11604055)
文摘A new normalized least mean square(NLMS) adaptive filter is first derived from a cost function, which incorporates the conventional one of the NLMS with a minimum-disturbance(MD)constraint. A variable regularization factor(RF) is then employed to control the contribution made by the MD constraint in the cost function. Analysis results show that the RF can be taken as a combination of the step size and regularization parameter in the conventional NLMS. This implies that these parameters can be jointly controlled by simply tuning the RF as the proposed algorithm does. It also demonstrates that the RF can accelerate the convergence rate of the proposed algorithm and its optimal value can be obtained by minimizing the squared noise-free posteriori error. A method for automatically determining the value of the RF is also presented, which is free of any prior knowledge of the noise. While simulation results verify the analytical ones, it is also illustrated that the performance of the proposed algorithm is superior to the state-of-art ones in both the steady-state misalignment and the convergence rate. A novel algorithm is proposed to solve some problems. Simulation results show the effectiveness of the proposed algorithm.
基金Natural Science Foundation of Shandong Province of China(No.ZR2012FM011)Shandong University of Science and Technology Research Fund(No.2010KYTD101)
文摘By analyzing algorithms available for variable step size least mean square(LMS)adaptive filter,a new modified LMS adaptive filtering algorithm with variable step size is proposed,along with performance analysis based on different parameters.Compared with the existing algorithms through the simulation,the proposed algorithm has faster convergence speed and smaller steady state error.
基金the National Natural Science Foundation of China(No.51575328,61503232).
文摘The contradiction of variable step size least mean square(LMS)algorithm between fast convergence speed and small steady-state error has always existed.So,a new algorithm based on the combination of logarithmic and symbolic function and step size factor is proposed.It establishes a new updating method of step factor that is related to step factor and error signal.This work makes an analysis from 3 aspects:theoretical analysis,theoretical verification and specific experiments.The experimental results show that the proposed algorithm is superior to other variable step size algorithms in convergence speed and steady-state error.
基金Supported by Natural Science Foundation of Beijing of China (No.2005AA501140)
文摘This paper puts forward a new variable step size LMS adaptive algorithm based on variable region. The step size p（k） in the algorithm varies with the variation of the region of deviation e （k） to ensure the optimization of the three performance objectives including initial convergent speed, trace ability of the time-varying system and steady disregulation. The paper demonstrates the convergence of the algorithm accompanied by random noise,
文摘为了改进现有的变步长最小均方误差(least mean square,LMS)算法在低信噪比时性能较差的缺陷,提出了一种基于改进的双曲正切函数的变步长LMS算法,从理论分析和仿真实验两方面讨论了引入参数对算法收敛性、跟踪性、稳定性的影响及算法的抗干扰性。理论分析和仿真实验表明该算法在高低信噪比时均具有较快的收敛速度和跟踪速度以及较小的稳态误差和稳态失调,并且在低信噪比时该算法的收敛性、跟踪性、稳态性均优于其他多种变步长算法。
文摘针对固定步长最小均方(LMS,least mean square)算法以及变步长LMS算法在收敛速度与稳态误差性能方面的不足,本文提出了一种新的基于对数函数改进的LMS算法.由于该算法中不涉及指数的运算,使得算法的计算量大大下降,收敛速度更快.仿真结果表明,对数函数改进的LMS算法比基于反正切函数改进的LMS算法具有近似相同的稳态误差性能,然而本文算法收敛更快,速度平均提高1.5倍.并且比基于双曲正切函数改进的LMS算法中的稳态误差平均降低0.5倍,同时收敛速度平均提高1.0倍.
