In this paper after analyzing the adaptation process of the proportionate normalized least mean square(PNLMS) algorithm, a statistical model is obtained to describe the convergence process of each adaptive filter coef...In this paper after analyzing the adaptation process of the proportionate normalized least mean square(PNLMS) algorithm, a statistical model is obtained to describe the convergence process of each adaptive filter coefcient. Inspired by this result, a modified PNLMS algorithm based on precise magnitude estimate is proposed. The simulation results indicate that in contrast to the traditional PNLMS algorithm, the proposed algorithm achieves faster convergence speed in the initial convergence state and lower misalignment in the stead stage with much less computational complexity.展开更多
In order to improve the problem that the filtered-x least mean square(FxLMS)algorithm cannot take into account the convergence speed,steady-state error during active noise control.A piecewise variable step size FxLMS ...In order to improve the problem that the filtered-x least mean square(FxLMS)algorithm cannot take into account the convergence speed,steady-state error during active noise control.A piecewise variable step size FxLMS algorithm based on logarithmic function(PLFxLMS)is proposed,and the genetic algorithm are introduced to optimize the parameters of logarithmic variable step size FxLMS(LFxLMS),improved logarithmic variable step size Films(IFxLMS),and PLFxLMS algorithms.Bandlimited white noise is used as the input signal,FxLMS,LFxLMS,ILFxLMS,and PLFxLMS algorithms are used to conduct active noise control simulation,and the convergence speed and steady-state characteristic of four algorithms are comparatively analyzed.Compared with the other three algorithms,the PLFxLMS algorithm proposed in this paper has the fastest convergence speed,and small steady-state error.The PLFxLMS algorithm can effectively improve the convergence speed and steady-state error of the FxLMS algorithm that cannot be controlled at the same time,and achieve the optimal effect.展开更多
Advanced processing of lung sound (LS) recording is a significant means to separate heart sounds (HS) and combined low frequency noise from instruments (NI), with saving its characteristics. This paper proposes a new ...Advanced processing of lung sound (LS) recording is a significant means to separate heart sounds (HS) and combined low frequency noise from instruments (NI), with saving its characteristics. This paper proposes a new method of LS filtering which separates HS and NI simultaneously. It focuses on the application of least mean squares (LMS) algorithm with adaptive noise cancelling (ANC) technique. The second step of the new method is to modulate the reference input r1(n) of LMS-ANC to acquiesce combining HS and NI signals. The obtained signal is removed from primary signal (original lung sound recording-LS). The original signal is recorded from subjects and derived HS from it and it is modified by a band pass filter. NI is simulated by generating approximately periodic white gaussian noise (WGN) signal. The LMS-ANC designed algorithm is controlled in order to determine the optimum values of the order L and the coefficient convergence μ. The output results are measured using power special density (PSD), which has shown the effectiveness of our suggested method. The result also has shown visual difference PSD (to) normal and abnormal LS recording. The results show that the method is a good technique for heart sound and noise reduction from lung sounds recordings simultaneously with saving LS characteristics.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
This paper proposes a novel de-noising algorithm based on ensemble empirical mode decomposition(EEMD) and the variable step size least mean square(VS-LMS) adaptive filter.The noise of the high frequency part of spectr...This paper proposes a novel de-noising algorithm based on ensemble empirical mode decomposition(EEMD) and the variable step size least mean square(VS-LMS) adaptive filter.The noise of the high frequency part of spectrum will be removed through EEMD,and then the VS-LMS algorithm is utilized for overall de-noising.The EEMD combined with VS-LMS algorithm can not only preserve the detail and envelope of the effective signal,but also improve the system stability.When the method is used on pure R6G,the signal-to-noise ratio(SNR) of Raman spectrum is lower than 10dB.The de-noising superiority of the proposed method in Raman spectrum can be verified by three evaluation standards of SNR,root mean square error(RMSE) and the correlation coefficient ρ.展开更多
The phenomenon of a target echo peak overlapping with the backscattered echo peak significantly undermines the detection range and precision of underwater laser fuzes.To overcome this issue,we propose a four-quadrant ...The phenomenon of a target echo peak overlapping with the backscattered echo peak significantly undermines the detection range and precision of underwater laser fuzes.To overcome this issue,we propose a four-quadrant dual-beam circumferential scanning laser fuze to distinguish various interference signals and provide more real-time data for the backscatter filtering algorithm.This enhances the algorithm loading capability of the fuze.In order to address the problem of insufficient filtering capacity in existing linear backscatter filtering algorithms,we develop a nonlinear backscattering adaptive filter based on the spline adaptive filter least mean square(SAF-LMS)algorithm.We also designed an algorithm pause module to retain the original trend of the target echo peak,improving the time discrimination accuracy and anti-interference capability of the fuze.Finally,experiments are conducted with varying signal-to-noise ratios of the original underwater target echo signals.The experimental results show that the average signal-to-noise ratio before and after filtering can be improved by more than31 d B,with an increase of up to 76%in extreme detection distance.展开更多
In an orthogonal frequency division multiplexing(OFDM) system,a time and frequency domain least mean square algorithm(TF-LMS) was proposed to cancel the frequency offset(FO).TF-LMS algorithm is composed of two stages....In an orthogonal frequency division multiplexing(OFDM) system,a time and frequency domain least mean square algorithm(TF-LMS) was proposed to cancel the frequency offset(FO).TF-LMS algorithm is composed of two stages.Firstly,time domain least mean square(TD-LMS) scheme was selected to pre-cancel the frequency offset in the time domain,and then the interference induced by residual frequency offset was eliminated by the frequency domain mean square(FD-LMS) scheme in frequency domain.The results of bit error rate(BER) and quadrature phase shift keying(QPSK) constellation figures show that the performance of the proposed suppression algorithm is excellent.展开更多
Aeromagnetic interference could not be compensated effectively if the precision of parameters which are solved by the aircraft magnetic field model is low. In order to improve the compensation effect under this condit...Aeromagnetic interference could not be compensated effectively if the precision of parameters which are solved by the aircraft magnetic field model is low. In order to improve the compensation effect under this condition, a method based on small signal model and least mean square（LMS） algorithm is proposed. According to the method, the initial values of adaptive filter＇s weight vector are calculated with the solved model parameters through small signal model at first,then the small amount of direction cosine and its derivative are set as the input of the filter, and the small amount of the interference is set as the filter＇s expected vector. After that, the aircraft magnetic interference is compensated by LMS algorithm. Finally, the method is verified by simulation and experiment. The result shows that the compensation effect can be improved obviously by the LMS algorithm when original solved parameters have low precision. The method can further improve the compensation effect even if the solved parameters have high precision.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
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 continuous stirred tank reactor(CSTR)is one of the typical chemical processes.Aiming at its strong nonlinear characteristics,a quantized kernel least mean square(QKLMS)algorithm is proposed.The QKLMS algorithm is ...The continuous stirred tank reactor(CSTR)is one of the typical chemical processes.Aiming at its strong nonlinear characteristics,a quantized kernel least mean square(QKLMS)algorithm is proposed.The QKLMS algorithm is based on a simple online vector quantization technology instead of sparsification,which can compress the input or feature space and suppress the growth of the radial basis function(RBF)structure in the kernel learning algorithm.To verify the effectiveness of the algorithm,it is applied to the model identification of CSTR process to construct a nonlinear mapping relationship between coolant flow rate and product concentration.In additiion,the proposed algorithm is further compared with least squares support vector machine(LS-SVM),echo state network(ESN),extreme learning machine with kernels(KELM),etc.The experimental results show that the proposed algorithm has higher identification accuracy and better online learning ability under the same conditions.展开更多
A multi-channel active vibration controller based on a filtered-u least mean square （FULMS） control algorithm is analyzed and implemented to solve the problem that the vibration feedback may affect the measuring of ...A multi-channel active vibration controller based on a filtered-u least mean square （FULMS） control algorithm is analyzed and implemented to solve the problem that the vibration feedback may affect the measuring of the reference signal of the filtered-x least mean square （FXLMS） algorithm in the field of active vibration control. By analyzing the multi-channel FULMS algorithm, the multi-channel controller structure diagram is given, while by analyzing multi-channel FXLMS algorithm and its algorithmic procedure, the control channel model identification strategy is given. This paper also provides an easy but practical way to configure the actuators based on the maximal modal force rule. Taking the configured piezoelectric beam as the research object, an active vibration control experimental platform is established to verify the effectiveness of the identification strategy as well as the FULMS control scheme. Simulation and actual control experiments are done after the model parameters are obtained. Both the simulation and actual experiment results show that the designed multi-channel vibration controller has a good control performance with low order model and rapid convergence.展开更多
In this paper, we consider the problem of unknown parameter estimation using a set of nodes that are deployed over an area. The recently proposed distributed adaptive estimation algorithms(also known as adaptive netwo...In this paper, we consider the problem of unknown parameter estimation using a set of nodes that are deployed over an area. The recently proposed distributed adaptive estimation algorithms(also known as adaptive networks) are appealing solutions to the mentioned problem when the statistical information of the underlying process is not available or it varies over time. In this paper, our goal is to develop a new incremental least-mean square(LMS) adaptive network that considers the quality of measurements collected by the nodes. Thus, we use an adaptive combination strategy which assigns each node a step size according to its quality of measurement. The adaptive combination strategy improves the robustness of the proposed algorithm to the spatial variations of signal-to-noise ratio(SNR). The performance of our algorithm is more remarkable in inhomogeneous environments when there are some nodes with low SNRs in the network. The simulation results indicate the efficiency of the proposed algorithm.展开更多
涡桨飞机座舱的主动噪声控制系统普遍采用传统的多通道滤波x最小均方(multichannel filtered-x least mean square,简称McFxLMS)算法,该算法的计算量随着通道数的增加而激增,严重影响控制效果。针对该问题,基于连续局部迭代-McFxLMS(seq...涡桨飞机座舱的主动噪声控制系统普遍采用传统的多通道滤波x最小均方(multichannel filtered-x least mean square,简称McFxLMS)算法,该算法的计算量随着通道数的增加而激增,严重影响控制效果。针对该问题,基于连续局部迭代-McFxLMS(sequential partial update-McFxLMS,简称SPU-McFxLMS)算法,开发了多通道主动噪声控制系统。SPU-McFxLMS算法通过更新部分滤波器权值,在保证收敛精度的同时能够显著降低计算复杂度。首先,对比分析了传统McFxLMS算法与SPU-McFxLMS算法的原理差异,通过理论推导证明其计算效率提升特性;其次,建立了算法仿真模型,通过仿真验证了理论分析结果;最后,基于SOM-TL6678核心板开发了16通道的主动噪声控制系统,并搭建飞机座舱地面模拟实验平台进行实验。结果表明,该系统在108 Hz和216 Hz双频噪声场景下,各位置的平均降噪量能够达到10 dB以上。展开更多
针对传统稀疏系统辨识算法对测量噪声和调谐参数敏感的问题,提出了一种全范围零吸引最小均方(full-range zero-attracting least mean square,FZA-LMS)稀疏系统辨识算法。该算法能够有效处理零吸引范围内的近零系数和小系数,进一步优化...针对传统稀疏系统辨识算法对测量噪声和调谐参数敏感的问题,提出了一种全范围零吸引最小均方(full-range zero-attracting least mean square,FZA-LMS)稀疏系统辨识算法。该算法能够有效处理零吸引范围内的近零系数和小系数,进一步优化零吸引范围外的大系数,降低稳态均方误差(mean square deviation,MSD),提升收敛速度,增强对调谐参数和测量噪声的鲁棒性。仿真实验结果表明,与传统稀疏系统辨识算法相比,提出的算法在稀疏声学回声信道下表现出更低的稳态MSD,对调谐参数和测量噪声具有更强的鲁棒性。针对水声信道估计场景下的复数信道,进一步提出了算法的复数形式。实验结果表明,在该场景下,复数形式算法相较于其他算法具有更优越的性能表现。展开更多
针对舰船直流供电系统中复杂谐波抑制难题,将滤波x最小均方(filtered-x least mean squares,FxLMS)算法应用于主动电力滤波器(active power filter,APF)以实现多频点谐波的主动抑制。通过构建仿真电路模型与决策树相位补偿方法,实现了...针对舰船直流供电系统中复杂谐波抑制难题,将滤波x最小均方(filtered-x least mean squares,FxLMS)算法应用于主动电力滤波器(active power filter,APF)以实现多频点谐波的主动抑制。通过构建仿真电路模型与决策树相位补偿方法,实现了次级通道相位变化的预补偿,确保了FxLMS算法在APF应用中的准确性。仿真结果表明:系统在0.3 s算法运行时间内成功达到了30 dB的抑制效果,应用效率高;系统对相位补偿与频率检测误差有显著适应性,在±60°的相位补偿误差范围、±3 Hz频点检测偏差范围内稳定性良好,可为系统在实际复杂环境中的应用提供有力保障。展开更多
文摘In this paper after analyzing the adaptation process of the proportionate normalized least mean square(PNLMS) algorithm, a statistical model is obtained to describe the convergence process of each adaptive filter coefcient. Inspired by this result, a modified PNLMS algorithm based on precise magnitude estimate is proposed. The simulation results indicate that in contrast to the traditional PNLMS algorithm, the proposed algorithm achieves faster convergence speed in the initial convergence state and lower misalignment in the stead stage with much less computational complexity.
文摘In order to improve the problem that the filtered-x least mean square(FxLMS)algorithm cannot take into account the convergence speed,steady-state error during active noise control.A piecewise variable step size FxLMS algorithm based on logarithmic function(PLFxLMS)is proposed,and the genetic algorithm are introduced to optimize the parameters of logarithmic variable step size FxLMS(LFxLMS),improved logarithmic variable step size Films(IFxLMS),and PLFxLMS algorithms.Bandlimited white noise is used as the input signal,FxLMS,LFxLMS,ILFxLMS,and PLFxLMS algorithms are used to conduct active noise control simulation,and the convergence speed and steady-state characteristic of four algorithms are comparatively analyzed.Compared with the other three algorithms,the PLFxLMS algorithm proposed in this paper has the fastest convergence speed,and small steady-state error.The PLFxLMS algorithm can effectively improve the convergence speed and steady-state error of the FxLMS algorithm that cannot be controlled at the same time,and achieve the optimal effect.
文摘Advanced processing of lung sound (LS) recording is a significant means to separate heart sounds (HS) and combined low frequency noise from instruments (NI), with saving its characteristics. This paper proposes a new method of LS filtering which separates HS and NI simultaneously. It focuses on the application of least mean squares (LMS) algorithm with adaptive noise cancelling (ANC) technique. The second step of the new method is to modulate the reference input r1(n) of LMS-ANC to acquiesce combining HS and NI signals. The obtained signal is removed from primary signal (original lung sound recording-LS). The original signal is recorded from subjects and derived HS from it and it is modified by a band pass filter. NI is simulated by generating approximately periodic white gaussian noise (WGN) signal. The LMS-ANC designed algorithm is controlled in order to determine the optimum values of the order L and the coefficient convergence μ. The output results are measured using power special density (PSD), which has shown the effectiveness of our suggested method. The result also has shown visual difference PSD (to) normal and abnormal LS recording. The results show that the method is a good technique for heart sound and noise reduction from lung sounds recordings simultaneously with saving LS characteristics.
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.61308120)the Doctor Startup Project of Xinjiang University(No.BS120122)+1 种基金the Young Talents Project in Xinjiang Uygur Autonomous Region(No.2013731003)the Xinjiang Science and Technology Project(Nos.201412107 and 2014211B003)
文摘This paper proposes a novel de-noising algorithm based on ensemble empirical mode decomposition(EEMD) and the variable step size least mean square(VS-LMS) adaptive filter.The noise of the high frequency part of spectrum will be removed through EEMD,and then the VS-LMS algorithm is utilized for overall de-noising.The EEMD combined with VS-LMS algorithm can not only preserve the detail and envelope of the effective signal,but also improve the system stability.When the method is used on pure R6G,the signal-to-noise ratio(SNR) of Raman spectrum is lower than 10dB.The de-noising superiority of the proposed method in Raman spectrum can be verified by three evaluation standards of SNR,root mean square error(RMSE) and the correlation coefficient ρ.
基金supported by the 2021 Open Project Fund of Science and Technology on Electromechanical Dynamic Control Laboratory,grant number 212-C-J-F-QT-2022-0020China Postdoctoral Science Foundation,grant number 2021M701713+1 种基金Postgraduate Research&Practice Innovation Program of Jiangsu Province,grant number KYCX23_0511the Jiangsu Funding Program for Excellent Postdoctoral Talent,grant number 20220ZB245。
文摘The phenomenon of a target echo peak overlapping with the backscattered echo peak significantly undermines the detection range and precision of underwater laser fuzes.To overcome this issue,we propose a four-quadrant dual-beam circumferential scanning laser fuze to distinguish various interference signals and provide more real-time data for the backscatter filtering algorithm.This enhances the algorithm loading capability of the fuze.In order to address the problem of insufficient filtering capacity in existing linear backscatter filtering algorithms,we develop a nonlinear backscattering adaptive filter based on the spline adaptive filter least mean square(SAF-LMS)algorithm.We also designed an algorithm pause module to retain the original trend of the target echo peak,improving the time discrimination accuracy and anti-interference capability of the fuze.Finally,experiments are conducted with varying signal-to-noise ratios of the original underwater target echo signals.The experimental results show that the average signal-to-noise ratio before and after filtering can be improved by more than31 d B,with an increase of up to 76%in extreme detection distance.
基金Project(60532030) supported by the National Natural Science Foundation of China
文摘In an orthogonal frequency division multiplexing(OFDM) system,a time and frequency domain least mean square algorithm(TF-LMS) was proposed to cancel the frequency offset(FO).TF-LMS algorithm is composed of two stages.Firstly,time domain least mean square(TD-LMS) scheme was selected to pre-cancel the frequency offset in the time domain,and then the interference induced by residual frequency offset was eliminated by the frequency domain mean square(FD-LMS) scheme in frequency domain.The results of bit error rate(BER) and quadrature phase shift keying(QPSK) constellation figures show that the performance of the proposed suppression algorithm is excellent.
基金co-supported by the National Basic Research Program of China (No. 623125020103)
文摘Aeromagnetic interference could not be compensated effectively if the precision of parameters which are solved by the aircraft magnetic field model is low. In order to improve the compensation effect under this condition, a method based on small signal model and least mean square（LMS） algorithm is proposed. According to the method, the initial values of adaptive filter＇s weight vector are calculated with the solved model parameters through small signal model at first,then the small amount of direction cosine and its derivative are set as the input of the filter, and the small amount of the interference is set as the filter＇s expected vector. After that, the aircraft magnetic interference is compensated by LMS algorithm. Finally, the method is verified by simulation and experiment. The result shows that the compensation effect can be improved obviously by the LMS algorithm when original solved parameters have low precision. The method can further improve the compensation effect even if the solved parameters have high precision.
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National Natural Science Foundation of China(No.51467008)Scientific Research Projects of Colleges and Universities in Gansu Province(Nos.2018C-10,2017D-09)。
文摘The continuous stirred tank reactor(CSTR)is one of the typical chemical processes.Aiming at its strong nonlinear characteristics,a quantized kernel least mean square(QKLMS)algorithm is proposed.The QKLMS algorithm is based on a simple online vector quantization technology instead of sparsification,which can compress the input or feature space and suppress the growth of the radial basis function(RBF)structure in the kernel learning algorithm.To verify the effectiveness of the algorithm,it is applied to the model identification of CSTR process to construct a nonlinear mapping relationship between coolant flow rate and product concentration.In additiion,the proposed algorithm is further compared with least squares support vector machine(LS-SVM),echo state network(ESN),extreme learning machine with kernels(KELM),etc.The experimental results show that the proposed algorithm has higher identification accuracy and better online learning ability under the same conditions.
基金Supported by the National Natural Science Foundation of China （No. 90716027, 51175319）, and Shanghai Talent Development Fund （No.2009020）.
文摘A multi-channel active vibration controller based on a filtered-u least mean square （FULMS） control algorithm is analyzed and implemented to solve the problem that the vibration feedback may affect the measuring of the reference signal of the filtered-x least mean square （FXLMS） algorithm in the field of active vibration control. By analyzing the multi-channel FULMS algorithm, the multi-channel controller structure diagram is given, while by analyzing multi-channel FXLMS algorithm and its algorithmic procedure, the control channel model identification strategy is given. This paper also provides an easy but practical way to configure the actuators based on the maximal modal force rule. Taking the configured piezoelectric beam as the research object, an active vibration control experimental platform is established to verify the effectiveness of the identification strategy as well as the FULMS control scheme. Simulation and actual control experiments are done after the model parameters are obtained. Both the simulation and actual experiment results show that the designed multi-channel vibration controller has a good control performance with low order model and rapid convergence.
文摘In this paper, we consider the problem of unknown parameter estimation using a set of nodes that are deployed over an area. The recently proposed distributed adaptive estimation algorithms(also known as adaptive networks) are appealing solutions to the mentioned problem when the statistical information of the underlying process is not available or it varies over time. In this paper, our goal is to develop a new incremental least-mean square(LMS) adaptive network that considers the quality of measurements collected by the nodes. Thus, we use an adaptive combination strategy which assigns each node a step size according to its quality of measurement. The adaptive combination strategy improves the robustness of the proposed algorithm to the spatial variations of signal-to-noise ratio(SNR). The performance of our algorithm is more remarkable in inhomogeneous environments when there are some nodes with low SNRs in the network. The simulation results indicate the efficiency of the proposed algorithm.
文摘涡桨飞机座舱的主动噪声控制系统普遍采用传统的多通道滤波x最小均方(multichannel filtered-x least mean square,简称McFxLMS)算法,该算法的计算量随着通道数的增加而激增,严重影响控制效果。针对该问题,基于连续局部迭代-McFxLMS(sequential partial update-McFxLMS,简称SPU-McFxLMS)算法,开发了多通道主动噪声控制系统。SPU-McFxLMS算法通过更新部分滤波器权值,在保证收敛精度的同时能够显著降低计算复杂度。首先,对比分析了传统McFxLMS算法与SPU-McFxLMS算法的原理差异,通过理论推导证明其计算效率提升特性;其次,建立了算法仿真模型,通过仿真验证了理论分析结果;最后,基于SOM-TL6678核心板开发了16通道的主动噪声控制系统,并搭建飞机座舱地面模拟实验平台进行实验。结果表明,该系统在108 Hz和216 Hz双频噪声场景下,各位置的平均降噪量能够达到10 dB以上。
文摘针对传统稀疏系统辨识算法对测量噪声和调谐参数敏感的问题,提出了一种全范围零吸引最小均方(full-range zero-attracting least mean square,FZA-LMS)稀疏系统辨识算法。该算法能够有效处理零吸引范围内的近零系数和小系数,进一步优化零吸引范围外的大系数,降低稳态均方误差(mean square deviation,MSD),提升收敛速度,增强对调谐参数和测量噪声的鲁棒性。仿真实验结果表明,与传统稀疏系统辨识算法相比,提出的算法在稀疏声学回声信道下表现出更低的稳态MSD,对调谐参数和测量噪声具有更强的鲁棒性。针对水声信道估计场景下的复数信道,进一步提出了算法的复数形式。实验结果表明,在该场景下,复数形式算法相较于其他算法具有更优越的性能表现。
文摘针对舰船直流供电系统中复杂谐波抑制难题,将滤波x最小均方(filtered-x least mean squares,FxLMS)算法应用于主动电力滤波器(active power filter,APF)以实现多频点谐波的主动抑制。通过构建仿真电路模型与决策树相位补偿方法,实现了次级通道相位变化的预补偿,确保了FxLMS算法在APF应用中的准确性。仿真结果表明:系统在0.3 s算法运行时间内成功达到了30 dB的抑制效果,应用效率高;系统对相位补偿与频率检测误差有显著适应性,在±60°的相位补偿误差范围、±3 Hz频点检测偏差范围内稳定性良好,可为系统在实际复杂环境中的应用提供有力保障。
