随着先进工艺和技术的不断进步,要想保证数据在高速传输中的正确性,均衡器需要有更高的补偿和更低的功耗,才能实现高效通信。基于12 nm互补金属氧化物半导体工艺,设计了一种高增益、低功耗的自适应连续时间线性均衡器(continuous time l...随着先进工艺和技术的不断进步,要想保证数据在高速传输中的正确性,均衡器需要有更高的补偿和更低的功耗,才能实现高效通信。基于12 nm互补金属氧化物半导体工艺,设计了一种高增益、低功耗的自适应连续时间线性均衡器(continuous time linear equalizer,CTLE),该均衡器采用2级级联结构来补偿信道衰减,并提高接收信号的质量。此外,自适应模块通过采用符号-符号最小均方误差(sign-sign least mean square,SS-LMS)算法,使抽头系数加快了收敛速度。仿真结果表明,当传输速率为16 Gbit/s时,均衡器可以补偿-15.53 dB的半波特率通道衰减,均衡器系数在16×10^(4)个单元间隔数据内收敛,并且收敛之后接收误码率低于10^(-12)。展开更多
针对现有超声测厚系统精度较低的问题,研制基于最小均方(Least Mean Square,LMS)自适应时间延迟估计的高精度超声测厚系统。设计超声发射及接收电路,研发基于现场可编程门阵列(Field Programmable Gate Array,FPGA)的高速数据采集及传...针对现有超声测厚系统精度较低的问题,研制基于最小均方(Least Mean Square,LMS)自适应时间延迟估计的高精度超声测厚系统。设计超声发射及接收电路,研发基于现场可编程门阵列(Field Programmable Gate Array,FPGA)的高速数据采集及传输系统,开发基于MATLAB的上位机信号处理软件,通过LMS自适应时间延迟估计算法准确、高效地计算超声飞行时间(Time of Flight,TOF),从而实现高精度厚度测量。开展模拟回波仿真实验,结果显示:相较峰值法、包络法和相关法,LMS法在时间延迟估计方面更具优势。搭建基于LMS自适应时延估计的超声测厚系统,以量块为对象进行测厚实验,结果表明:该系统测厚相对误差小于3.77%,重复实验标准差不高于0.2μm,最大相对不确定度为1.4%。基于LMS自适应时延估计的超声测厚系统可应用于板材厚度测量等领域,有利于推动高精度超声测厚技术发展,具有重要技术借鉴价值。展开更多
论文研究了自适应最小均方误差(Least Mean Squares,LMS)滤波算法的步长选取问题。在分析现有算法的基础上,通过构造步长与误差信号之间的非线性函数,提出一种新的变步长LMS算法。新算法采用误差信号的自相关估计值控制步长,而不是直接...论文研究了自适应最小均方误差(Least Mean Squares,LMS)滤波算法的步长选取问题。在分析现有算法的基础上,通过构造步长与误差信号之间的非线性函数,提出一种新的变步长LMS算法。新算法采用误差信号的自相关估计值控制步长,而不是直接利用瞬时误差控制步长,避免了噪声干扰,降低了稳态失调,可工作于低信噪比环境。同时新算法步长控制无记忆效应,提高了收敛速度。仿真表明,新算法的稳态失调和收敛速度均优于现有变步长LMS算法。展开更多
针对风洞试验模型系统辨识不准确的问题,利用自适应LMS(least mean square)滤波器模型对跨声速风洞模型进行系统辨识。由于实测信号中存在多模态耦合,为了提高系统辨识精准度,首先对输入输出信号作了FRF(frequency response analysis)...针对风洞试验模型系统辨识不准确的问题,利用自适应LMS(least mean square)滤波器模型对跨声速风洞模型进行系统辨识。由于实测信号中存在多模态耦合,为了提高系统辨识精准度,首先对输入输出信号作了FRF(frequency response analysis)分析得到试验模型俯仰方向前两阶模态,其次利用快速Fourier变换进行模态解耦,接着利用自适应LMS滤波器模型、传递函数模型、多项式模型对俯仰方向单模态进行系统辨识,最后得到了基于自适应LMS滤波器模型的俯仰方向一阶、二阶模态滤波器系数。通过对比不同数学模型的输出与输入之间的相关系数和均方误差及辨识结果,表明自适应LMS滤波器模型具有更高的系统辨识精准度和更简洁的数学模型结构。为后续风洞试验模型振动主动控制计算法的设计提供有力支撑。展开更多
Random vibration control is aimed at reproducing the power spectral density （PSD） at specified control points. The classical frequency-spectrum equalization algorithm needs to compute the average of the multiple fre...Random vibration control is aimed at reproducing the power spectral density （PSD） at specified control points. The classical frequency-spectrum equalization algorithm needs to compute the average of the multiple frequency response functions （FRFs）, which lengthens the control loop time in the equalization process. Likewise, the feedback control algorithm has a very slow convergence rate due to the small value of the feedback gain parameter to ensure stability of the system. To overcome these limitations, an adaptive inverse control of random vibrations based on the filtered-X least mean-square （LMS） algorithm is proposed. Furthermore, according to the description and iteration characteristics of random vibration tests in the frequency domain, the frequency domain LMS algorithm is adopted to refine the inverse characteristics of the FRF instead of the traditional time domain LMS algorithm. This inverse characteristic, which is called the impedance function of the system under control, is used to update the drive PSD directly. The test results indicated that in addition to successfully avoiding the instability problem that occurs during the iteration process, the adaptive control strategy minimizes the amount of time needed to obtain a short control loop and achieve equalization.展开更多
A Matrix Inversion Normalized Least Mean Square (MI-NLMS) adaptive beamforming algorithm was developed for smart antenna application. The MI-NLMS which combined the individual good aspects of Sample Matrix Inversion (...A Matrix Inversion Normalized Least Mean Square (MI-NLMS) adaptive beamforming algorithm was developed for smart antenna application. The MI-NLMS which combined the individual good aspects of Sample Matrix Inversion (SMI) and the Normalized Least Mean Square (NLMS) algorithms is described. Simulation results showed that the less complexity MI-NLMS yields 15 dB improvements in interference suppression and 5 dB gain enhancement over LMS algorithm, converges from the initial iteration and achieves 24% BER improvements at cochannel interference equal to 5. For the case of 4-element uniform linear array antenna, MI-NLMS achieved 76% BER reduction over LMS algorithm.展开更多
Underwater acoustic channels are recognized for being one of the most difficult propagation media due to considerable difficulties such as： multipath, ambient noise, time-frequency selective fading. The exploitation ...Underwater acoustic channels are recognized for being one of the most difficult propagation media due to considerable difficulties such as： multipath, ambient noise, time-frequency selective fading. The exploitation of sparsity contained in underwater acoustic channels provides a potential solution to improve the performance of underwater acoustic channel estimation. Compared with the classic 10 and 11 norm constraint LMS algorithms, the p-norm-like （Ip） constraint LMS algorithm proposed in our previous investigation exhibits better sparsity exploitation performance at the presence of channel variations, as it enables the adaptability to the sparseness by tuning of p parameter. However, the decimal exponential calculation associated with the p-norm-like constraint LMS algorithm poses considerable limitations in practical application. In this paper, a simplified variant of the p-norm-like constraint LMS was proposed with the employment of Newton iteration m to approximate the decimal exponential calculation. Num simulations and the experimental results obtained in physical shallow water channels demonstrate the effectiveness of the proposed method compared to traditional norm constraint LMS algorithms.展开更多
针对机载设备的BIT系统,提出了一种改进的LMS(Least Mean Squares)算法,该算法主要应用于故障检测和定位方面。通过引入自适应学习率控制、加速收敛和稳定性优化技术手段,该算法能够显著提升BIT系统中的信号处理性能,加快故障检测和定...针对机载设备的BIT系统,提出了一种改进的LMS(Least Mean Squares)算法,该算法主要应用于故障检测和定位方面。通过引入自适应学习率控制、加速收敛和稳定性优化技术手段,该算法能够显著提升BIT系统中的信号处理性能,加快故障检测和定位速度,并提高系统的准确性和稳定性。在故障检测方面,改进的LMS算法可以有效地识别故障信号并进行分类和定位。通过对输入信号进行预处理和模型参数的优化,改进LMS算法能够更准确地捕捉异常信号特征,从而实现对故障的快速检测和定位,提高BIT系统的可靠性和故障诊断能力。同时,改进的LMS算法还应用于BIT系统中的自适应滤波模块,用于消除噪声和滤除干扰信号。通过采用自适应学习率控制和加速收敛技术,改进算法能够智能地调整滤波参数,有效抑制噪声和干扰信号,提高BIT系统对故障信号的识别和定位能力。通过实验验证,改进的LMS算法在机载设备的BIT系统中表现出较好的应用潜力。该算法相比传统LMS算法,在故障检测和定位准确性、故障诊断速度以及系统稳定性方面均取得了显著的改善。展开更多
文摘随着先进工艺和技术的不断进步,要想保证数据在高速传输中的正确性,均衡器需要有更高的补偿和更低的功耗,才能实现高效通信。基于12 nm互补金属氧化物半导体工艺,设计了一种高增益、低功耗的自适应连续时间线性均衡器(continuous time linear equalizer,CTLE),该均衡器采用2级级联结构来补偿信道衰减,并提高接收信号的质量。此外,自适应模块通过采用符号-符号最小均方误差(sign-sign least mean square,SS-LMS)算法,使抽头系数加快了收敛速度。仿真结果表明,当传输速率为16 Gbit/s时,均衡器可以补偿-15.53 dB的半波特率通道衰减,均衡器系数在16×10^(4)个单元间隔数据内收敛,并且收敛之后接收误码率低于10^(-12)。
文摘针对现有超声测厚系统精度较低的问题,研制基于最小均方(Least Mean Square,LMS)自适应时间延迟估计的高精度超声测厚系统。设计超声发射及接收电路,研发基于现场可编程门阵列(Field Programmable Gate Array,FPGA)的高速数据采集及传输系统,开发基于MATLAB的上位机信号处理软件,通过LMS自适应时间延迟估计算法准确、高效地计算超声飞行时间(Time of Flight,TOF),从而实现高精度厚度测量。开展模拟回波仿真实验,结果显示:相较峰值法、包络法和相关法,LMS法在时间延迟估计方面更具优势。搭建基于LMS自适应时延估计的超声测厚系统,以量块为对象进行测厚实验,结果表明:该系统测厚相对误差小于3.77%,重复实验标准差不高于0.2μm,最大相对不确定度为1.4%。基于LMS自适应时延估计的超声测厚系统可应用于板材厚度测量等领域,有利于推动高精度超声测厚技术发展,具有重要技术借鉴价值。
文摘论文研究了自适应最小均方误差(Least Mean Squares,LMS)滤波算法的步长选取问题。在分析现有算法的基础上,通过构造步长与误差信号之间的非线性函数,提出一种新的变步长LMS算法。新算法采用误差信号的自相关估计值控制步长,而不是直接利用瞬时误差控制步长,避免了噪声干扰,降低了稳态失调,可工作于低信噪比环境。同时新算法步长控制无记忆效应,提高了收敛速度。仿真表明,新算法的稳态失调和收敛速度均优于现有变步长LMS算法。
文摘针对风洞试验模型系统辨识不准确的问题,利用自适应LMS(least mean square)滤波器模型对跨声速风洞模型进行系统辨识。由于实测信号中存在多模态耦合,为了提高系统辨识精准度,首先对输入输出信号作了FRF(frequency response analysis)分析得到试验模型俯仰方向前两阶模态,其次利用快速Fourier变换进行模态解耦,接着利用自适应LMS滤波器模型、传递函数模型、多项式模型对俯仰方向单模态进行系统辨识,最后得到了基于自适应LMS滤波器模型的俯仰方向一阶、二阶模态滤波器系数。通过对比不同数学模型的输出与输入之间的相关系数和均方误差及辨识结果,表明自适应LMS滤波器模型具有更高的系统辨识精准度和更简洁的数学模型结构。为后续风洞试验模型振动主动控制计算法的设计提供有力支撑。
基金Program for New Century Excellent Talents in Universities Under Grant No.NCET-04-0325
文摘Random vibration control is aimed at reproducing the power spectral density （PSD） at specified control points. The classical frequency-spectrum equalization algorithm needs to compute the average of the multiple frequency response functions （FRFs）, which lengthens the control loop time in the equalization process. Likewise, the feedback control algorithm has a very slow convergence rate due to the small value of the feedback gain parameter to ensure stability of the system. To overcome these limitations, an adaptive inverse control of random vibrations based on the filtered-X least mean-square （LMS） algorithm is proposed. Furthermore, according to the description and iteration characteristics of random vibration tests in the frequency domain, the frequency domain LMS algorithm is adopted to refine the inverse characteristics of the FRF instead of the traditional time domain LMS algorithm. This inverse characteristic, which is called the impedance function of the system under control, is used to update the drive PSD directly. The test results indicated that in addition to successfully avoiding the instability problem that occurs during the iteration process, the adaptive control strategy minimizes the amount of time needed to obtain a short control loop and achieve equalization.
基金Project supported by the IRPA Secretariat, Ministry of Science,Technology and Environment of Malaysia (No. 04-02-02-0029) andthe Zamalah Scheme
文摘A Matrix Inversion Normalized Least Mean Square (MI-NLMS) adaptive beamforming algorithm was developed for smart antenna application. The MI-NLMS which combined the individual good aspects of Sample Matrix Inversion (SMI) and the Normalized Least Mean Square (NLMS) algorithms is described. Simulation results showed that the less complexity MI-NLMS yields 15 dB improvements in interference suppression and 5 dB gain enhancement over LMS algorithm, converges from the initial iteration and achieves 24% BER improvements at cochannel interference equal to 5. For the case of 4-element uniform linear array antenna, MI-NLMS achieved 76% BER reduction over LMS algorithm.
基金Supported by the National Natural Science Foundation of China （No.11274259） and the Specialized Research Foundation for the Doctoral Program of Higher Education of China （No.20120121110030）.
文摘Underwater acoustic channels are recognized for being one of the most difficult propagation media due to considerable difficulties such as： multipath, ambient noise, time-frequency selective fading. The exploitation of sparsity contained in underwater acoustic channels provides a potential solution to improve the performance of underwater acoustic channel estimation. Compared with the classic 10 and 11 norm constraint LMS algorithms, the p-norm-like （Ip） constraint LMS algorithm proposed in our previous investigation exhibits better sparsity exploitation performance at the presence of channel variations, as it enables the adaptability to the sparseness by tuning of p parameter. However, the decimal exponential calculation associated with the p-norm-like constraint LMS algorithm poses considerable limitations in practical application. In this paper, a simplified variant of the p-norm-like constraint LMS was proposed with the employment of Newton iteration m to approximate the decimal exponential calculation. Num simulations and the experimental results obtained in physical shallow water channels demonstrate the effectiveness of the proposed method compared to traditional norm constraint LMS algorithms.
文摘针对机载设备的BIT系统,提出了一种改进的LMS(Least Mean Squares)算法,该算法主要应用于故障检测和定位方面。通过引入自适应学习率控制、加速收敛和稳定性优化技术手段,该算法能够显著提升BIT系统中的信号处理性能,加快故障检测和定位速度,并提高系统的准确性和稳定性。在故障检测方面,改进的LMS算法可以有效地识别故障信号并进行分类和定位。通过对输入信号进行预处理和模型参数的优化,改进LMS算法能够更准确地捕捉异常信号特征,从而实现对故障的快速检测和定位,提高BIT系统的可靠性和故障诊断能力。同时,改进的LMS算法还应用于BIT系统中的自适应滤波模块,用于消除噪声和滤除干扰信号。通过采用自适应学习率控制和加速收敛技术,改进算法能够智能地调整滤波参数,有效抑制噪声和干扰信号,提高BIT系统对故障信号的识别和定位能力。通过实验验证,改进的LMS算法在机载设备的BIT系统中表现出较好的应用潜力。该算法相比传统LMS算法,在故障检测和定位准确性、故障诊断速度以及系统稳定性方面均取得了显著的改善。
