基于自适应滤波及模态分析的有源噪声控制方法研究
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摘要
随着经济的发展和社会的进步，噪声污染日益严重，危害人们的身心健康，引起机械设备结构的声疲劳，并降低武器装备的作战性能和声隐身性能。因此噪声控制在民用和军事领域中都具有十分重要的意义。有源噪声控制技术是控制低频噪声的有效方法，根据噪声控制机理可分为声源控制、传播途径控制和接受者保护三类，研究内容和手段具有多样性。论文从接受者保护和声源控制的角度出发，针对基于自适应滤波技术的前馈有源噪声控制方法和基于模态分析的结构声辐射控制方法中存在的一些主要问题开展研究工作。论文所做主要工作概况如下：
1.在自适应前馈有源控制技术中，非因果成分会降低噪声控制性能，为了使系统尽量满足因果约束，提出一种参考信号的拾取方法。该方法将参考传声器放置在噪声源来波方向上，并通过距离调整来增大参考传声器和误差传声器的时间延迟，用于抵消串扰和反射等因素引起的非因果成分，从而提高噪声控制性能。在此方法中，为了检测宽带噪声源的来波方向，提出了一种基于KR积的内插聚焦估计算法。该算法将各频率点的协方差矩阵进行KR积扩展后，借助最小二乘内插聚焦矩阵，构造无噪声聚焦后的各频率数据组合矩阵，进而采用MUSIC方法实现过定和欠定两种情况下的来波方向估计。最后，将噪声源的定位结果用于指导参考传声器的布放，通过比较传声器布放位置及数目调整前后的噪声控制效果，验证参考信号拾取方法的有效性。
2.在宽带噪声的自适应前馈有源控制技术中，无延迟子带自适应滤波方法由于能避免信号通道的延迟且收敛速度快，成为宽带噪声控制中的主要方法。但该方法的控制效果会受到分析滤波器组的群延迟和带内混叠误差的影响，因此分析滤波组的设计是关键。现有的分析滤波器组是调制线性相位原型低通滤波器得到的，它不能保证滤波器组的低群延迟和小旁瓣效应，致使噪声控制效果不明显。为此，提出迭代二阶锥规划方法设计分析滤波器组。将带内混叠误差和群延迟构造为关于原型滤波器权系数的二次函数，进而通过迭代二阶锥优化得到原型滤波器权系数，最后利用均匀DFT调制为分析滤波器组。与同阶线性相位分析滤波器组相比，该滤波器组具有更低的群延迟和更小的带内混叠误差。在此基础上，采用部分子带权系数更新的归一化最小均方误差算法，构建了频域无延迟噪声控制系统。实验结果表明，该系统的噪声控制效果明显，不仅能有效降低残余噪声功率谱，提高噪声衰减量，而且还具有收敛速度快和计算复杂度低等优点。
3．在窄带噪声的自适应前馈有源控制技术中，从理论上分析了频率偏差造成自适应滤波器权系数的均值偏移不能收敛到零矢量，是导致控制性能恶化的根本原因。因此为了提高噪声控制效果，必须对干扰信号的频率进行精确估计。为此，提出了一种用于实值信号的快速子空间跟踪-ESPRIT类频率估计方法。该方法的信号子空间维数与信号频率的个数相等，从而避免了传统方法用于实值频率估计时，信号子空间的维数为频率个数2倍的问题。仿真结果表明算法的精度高，满足实际应用的要求。最后，结合不需要次级通道估计的FXLMS算法，在DSP平台上实现了弹性薄板的基频和谐波分量的振动控制，从而降低了封闭声腔中的结构声辐射的声压级。
4．在基于模态分析的弹性结构声辐射的控制技术中，根据输入输出测量数据建立精确受控系统模型是实施该类控制方法的前提。目前，主要采用子空间辨识方法来建立系统状态方程，但该方法计算量大，难以得到有效应用。为此，利用广义能观矩阵的列空间与观测矢量相关矩阵的信号子空间一致的特征，提出了一种在线子空间辨识方法用于系统状态方程的建立。该方法包含了两个递推过程：一是利用矩阵求逆变换计算观测矢量；二是基于多级分解幂迭代子空间跟踪算法估计广义能观矩阵，以保证按指数全局收敛到主子空间。仿真结果表明该方法能提高辨识精度，不仅减小广义能观矩阵估计值与真实值的夹角，还能降低辨识得到的输出数据与真实值之间的均方根误差。最后，利用有限元和声学分析软件，构建了一种基于振动控制的封闭空间降噪系统模拟环境。采用所提的在线子空间辨识法建立了压电结构系统模型，结合线性二次最优控制方法产生压电陶瓷作动器次级电压。通过控制与声模态耦合大的结构模态，有效降低了舱室结构声辐射的声压级，验证了在线子空间辨识方法应用在结构声辐射控制中的有效性。
With the economic development and social progress, the noise pollution isbecoming more and more serious problem. Noise harms people's physical and mentalhealth, causes acoustic fatigue of mechanical equipments. Also, it degrades theoperation and stealth performance of weapon equipments. Consequently, reducing theeffect of noise has important practical significance in civil and military fields. Theactive noise control technique is an effective method for low frequency noise control.According to the mechanism of noise control, the technique can be divided into threeclasses: source control, transmission control and receiver protection. This thesisthoroughly investigates the feedforward active noise control method based on adaptivefiltering, and structural acoustic radiation control method based on modal analysis, torealize the receiver protection and source control. The main content of this thesis can besummarized as follows:
1. In the adaptive feedforward active noise control system, the noise attenuation ofnoncausal system is less than that of causal system. In order to ensure the causality andenhance the controlling effectiveness, the pickup method of the reference signal isdiscussed. The reference microphones are demanded to be placed in the directions ofarrival (DOA) of noise sources. The time delay between the reference microphones andthe error microphones are increased by enlarging the distances between them, used tooffset the noncausal components caused by echo and crosstalk in the control paths.Accordingly, it is necessary to estimate the DOA of noise sources in this pickup scheme.Based on Khatri-Rao (KR) product and interpolation focusing matrix, a new algorithmis proposed to estimate the DOA. In this algorithm, the covariance matrix at eachfrequency point is stacked into column vectors through KR product. Then the columnvectors are focused on the center frequency to construct a noise-free data matrix, using aleast square interpolation focusing matrix. Finally, the DOA is estimated by the methodMUSIC in the overdetermined and underdetermined cases. With DOA estimation for theplacement of reference microphones, the experiment results verify the feasibility of theproposed pickup method, by comparing the noise control performance before and afterthe adjustment of location and number for the reference microphones.
2. The delayless subband adaptive filtering technique plays a prominent role in thewideband noise active control, which can avoid the signal path delay and accelerate theconverging rate of filter weights. The noise controlling performance of this technique isaffected by the group delay and in-band aliasing of analysis filter banks. Therefore, the design of the analysis filter banks is very crucial. Typically, the analysis filter banks arecomposed of a set of filters created by modulating a prototype FIR filter (PF). Suchfilter banks suffer from a long group delay and a high side-lobe effect, which limits thenoise attenuation. An iterative second-order cone programming (SOCP) method isdeveloped to design the analysis filter banks. The in-band aliasing distortion and groupdelay constraints are expressed as the cost functions of the PF coefficients. In the sequel,the optimization problem of the cost functions can be formulated as an iterative SOCPform. The optimized PF coefficients are uniform-DFT modulated into the desiredanalysis filter banks. The simulation results demonstrate that the well designed filterbanks have a smaller in-band aliasing distortion and lower group delay, compared withthe linear-phase filter banks. Finally, a structure of the delayless subband active noisecontrol in frequency domain is provided, merged with the selective partial updatenormalized LMS algorithm. Simulation results show that the noise controllingperformance is improved with a small residual noise power spectrum, high noiseattenuation level and fast convergence rate.
3. In the narrowband active noise control system, the frequency mismatch betweenthe synthesized reference signal and the primary noise will cause that the mean valuedeviation of filter weight coefficients cannot converge to zero vector. As a result, theperformance of narrowband active noise control systems degenerates significantly. Inorder to improve the performance, it is necessary to estimate the frequencies of theprimary noise. An ESPRIT-like algorithm with subspace tracking is proposed toestimate the frequencies of real-valued sinusoidal noise signal. In the proposedalgorithm, the dimension of the signal subspace is equal to the number of thefrequencies present in the observation, and a half of the signal subspace dimension inthe MUSIC method. The simulation results indicate that the proposed algorithmpossesses a high precision to meet the requirement of practical application. Equippedwith the digital signal processing (DSP) platform, the harmonic components ofvibration is controlled for the elastic thin plat through the FXLMS algorithm withoutsecondary path identification. Meanwhile the sound pressure level is reduced for thestructural acoustic radiation noise in the enclosure cavity.
4. In the control technique of structural acoustic radiation based on modal analysis, aprecise model is the prerequisite for application of the control method. Recently, thesubspace identification method is popular for establishing state space model. However,it is difficult for practical application because of huge complexity. In the basis of factthat the column space of the extended observability matrix is the same with the signal subspace of autocorrelation matrix of the observation vector, a new recursive subspaceidentification algorithm is proposed for the estimation of state space method. It containstwo recursive processes: one is to compute the observation vectors by using the matrixinversion formula, the other is to estimate the extended observability matrix based onthe multistage power iteration subspace tracking algorithm. Such algorithm canexponentially converge to the principal subspace. With the numerical and practicalmodels, the experiment results show that this method can enhance the identificationprecision. In detail, it manifests a small angle between the estimation and true extendedobservability matrix, and low root mean square error between the identified output andthe actual output. Also we simulate the control environment of the structural acousticradiation by using finite element and acoustic analysis software. The state space modelof the piezoelectric smart structure is identified by the proposed method, and the controlvoltage of the secondary actuator is computed by the linear quadratic optimal controlmethod. Finally, the sound pressure level of the structural acoustic radiation noise isdecreased by controlling the structure modals which have large coupling coefficientswith the acoustic modals.
1.在自适应前馈有源控制技术中，非因果成分会降低噪声控制性能，为了使系统尽量满足因果约束，提出一种参考信号的拾取方法。该方法将参考传声器放置在噪声源来波方向上，并通过距离调整来增大参考传声器和误差传声器的时间延迟，用于抵消串扰和反射等因素引起的非因果成分，从而提高噪声控制性能。在此方法中，为了检测宽带噪声源的来波方向，提出了一种基于KR积的内插聚焦估计算法。该算法将各频率点的协方差矩阵进行KR积扩展后，借助最小二乘内插聚焦矩阵，构造无噪声聚焦后的各频率数据组合矩阵，进而采用MUSIC方法实现过定和欠定两种情况下的来波方向估计。最后，将噪声源的定位结果用于指导参考传声器的布放，通过比较传声器布放位置及数目调整前后的噪声控制效果，验证参考信号拾取方法的有效性。
2.在宽带噪声的自适应前馈有源控制技术中，无延迟子带自适应滤波方法由于能避免信号通道的延迟且收敛速度快，成为宽带噪声控制中的主要方法。但该方法的控制效果会受到分析滤波器组的群延迟和带内混叠误差的影响，因此分析滤波组的设计是关键。现有的分析滤波器组是调制线性相位原型低通滤波器得到的，它不能保证滤波器组的低群延迟和小旁瓣效应，致使噪声控制效果不明显。为此，提出迭代二阶锥规划方法设计分析滤波器组。将带内混叠误差和群延迟构造为关于原型滤波器权系数的二次函数，进而通过迭代二阶锥优化得到原型滤波器权系数，最后利用均匀DFT调制为分析滤波器组。与同阶线性相位分析滤波器组相比，该滤波器组具有更低的群延迟和更小的带内混叠误差。在此基础上，采用部分子带权系数更新的归一化最小均方误差算法，构建了频域无延迟噪声控制系统。实验结果表明，该系统的噪声控制效果明显，不仅能有效降低残余噪声功率谱，提高噪声衰减量，而且还具有收敛速度快和计算复杂度低等优点。
3．在窄带噪声的自适应前馈有源控制技术中，从理论上分析了频率偏差造成自适应滤波器权系数的均值偏移不能收敛到零矢量，是导致控制性能恶化的根本原因。因此为了提高噪声控制效果，必须对干扰信号的频率进行精确估计。为此，提出了一种用于实值信号的快速子空间跟踪-ESPRIT类频率估计方法。该方法的信号子空间维数与信号频率的个数相等，从而避免了传统方法用于实值频率估计时，信号子空间的维数为频率个数2倍的问题。仿真结果表明算法的精度高，满足实际应用的要求。最后，结合不需要次级通道估计的FXLMS算法，在DSP平台上实现了弹性薄板的基频和谐波分量的振动控制，从而降低了封闭声腔中的结构声辐射的声压级。
4．在基于模态分析的弹性结构声辐射的控制技术中，根据输入输出测量数据建立精确受控系统模型是实施该类控制方法的前提。目前，主要采用子空间辨识方法来建立系统状态方程，但该方法计算量大，难以得到有效应用。为此，利用广义能观矩阵的列空间与观测矢量相关矩阵的信号子空间一致的特征，提出了一种在线子空间辨识方法用于系统状态方程的建立。该方法包含了两个递推过程：一是利用矩阵求逆变换计算观测矢量；二是基于多级分解幂迭代子空间跟踪算法估计广义能观矩阵，以保证按指数全局收敛到主子空间。仿真结果表明该方法能提高辨识精度，不仅减小广义能观矩阵估计值与真实值的夹角，还能降低辨识得到的输出数据与真实值之间的均方根误差。最后，利用有限元和声学分析软件，构建了一种基于振动控制的封闭空间降噪系统模拟环境。采用所提的在线子空间辨识法建立了压电结构系统模型，结合线性二次最优控制方法产生压电陶瓷作动器次级电压。通过控制与声模态耦合大的结构模态，有效降低了舱室结构声辐射的声压级，验证了在线子空间辨识方法应用在结构声辐射控制中的有效性。
With the economic development and social progress, the noise pollution isbecoming more and more serious problem. Noise harms people's physical and mentalhealth, causes acoustic fatigue of mechanical equipments. Also, it degrades theoperation and stealth performance of weapon equipments. Consequently, reducing theeffect of noise has important practical significance in civil and military fields. Theactive noise control technique is an effective method for low frequency noise control.According to the mechanism of noise control, the technique can be divided into threeclasses: source control, transmission control and receiver protection. This thesisthoroughly investigates the feedforward active noise control method based on adaptivefiltering, and structural acoustic radiation control method based on modal analysis, torealize the receiver protection and source control. The main content of this thesis can besummarized as follows:
1. In the adaptive feedforward active noise control system, the noise attenuation ofnoncausal system is less than that of causal system. In order to ensure the causality andenhance the controlling effectiveness, the pickup method of the reference signal isdiscussed. The reference microphones are demanded to be placed in the directions ofarrival (DOA) of noise sources. The time delay between the reference microphones andthe error microphones are increased by enlarging the distances between them, used tooffset the noncausal components caused by echo and crosstalk in the control paths.Accordingly, it is necessary to estimate the DOA of noise sources in this pickup scheme.Based on Khatri-Rao (KR) product and interpolation focusing matrix, a new algorithmis proposed to estimate the DOA. In this algorithm, the covariance matrix at eachfrequency point is stacked into column vectors through KR product. Then the columnvectors are focused on the center frequency to construct a noise-free data matrix, using aleast square interpolation focusing matrix. Finally, the DOA is estimated by the methodMUSIC in the overdetermined and underdetermined cases. With DOA estimation for theplacement of reference microphones, the experiment results verify the feasibility of theproposed pickup method, by comparing the noise control performance before and afterthe adjustment of location and number for the reference microphones.
2. The delayless subband adaptive filtering technique plays a prominent role in thewideband noise active control, which can avoid the signal path delay and accelerate theconverging rate of filter weights. The noise controlling performance of this technique isaffected by the group delay and in-band aliasing of analysis filter banks. Therefore, the design of the analysis filter banks is very crucial. Typically, the analysis filter banks arecomposed of a set of filters created by modulating a prototype FIR filter (PF). Suchfilter banks suffer from a long group delay and a high side-lobe effect, which limits thenoise attenuation. An iterative second-order cone programming (SOCP) method isdeveloped to design the analysis filter banks. The in-band aliasing distortion and groupdelay constraints are expressed as the cost functions of the PF coefficients. In the sequel,the optimization problem of the cost functions can be formulated as an iterative SOCPform. The optimized PF coefficients are uniform-DFT modulated into the desiredanalysis filter banks. The simulation results demonstrate that the well designed filterbanks have a smaller in-band aliasing distortion and lower group delay, compared withthe linear-phase filter banks. Finally, a structure of the delayless subband active noisecontrol in frequency domain is provided, merged with the selective partial updatenormalized LMS algorithm. Simulation results show that the noise controllingperformance is improved with a small residual noise power spectrum, high noiseattenuation level and fast convergence rate.
3. In the narrowband active noise control system, the frequency mismatch betweenthe synthesized reference signal and the primary noise will cause that the mean valuedeviation of filter weight coefficients cannot converge to zero vector. As a result, theperformance of narrowband active noise control systems degenerates significantly. Inorder to improve the performance, it is necessary to estimate the frequencies of theprimary noise. An ESPRIT-like algorithm with subspace tracking is proposed toestimate the frequencies of real-valued sinusoidal noise signal. In the proposedalgorithm, the dimension of the signal subspace is equal to the number of thefrequencies present in the observation, and a half of the signal subspace dimension inthe MUSIC method. The simulation results indicate that the proposed algorithmpossesses a high precision to meet the requirement of practical application. Equippedwith the digital signal processing (DSP) platform, the harmonic components ofvibration is controlled for the elastic thin plat through the FXLMS algorithm withoutsecondary path identification. Meanwhile the sound pressure level is reduced for thestructural acoustic radiation noise in the enclosure cavity.
4. In the control technique of structural acoustic radiation based on modal analysis, aprecise model is the prerequisite for application of the control method. Recently, thesubspace identification method is popular for establishing state space model. However,it is difficult for practical application because of huge complexity. In the basis of factthat the column space of the extended observability matrix is the same with the signal subspace of autocorrelation matrix of the observation vector, a new recursive subspaceidentification algorithm is proposed for the estimation of state space method. It containstwo recursive processes: one is to compute the observation vectors by using the matrixinversion formula, the other is to estimate the extended observability matrix based onthe multistage power iteration subspace tracking algorithm. Such algorithm canexponentially converge to the principal subspace. With the numerical and practicalmodels, the experiment results show that this method can enhance the identificationprecision. In detail, it manifests a small angle between the estimation and true extendedobservability matrix, and low root mean square error between the identified output andthe actual output. Also we simulate the control environment of the structural acousticradiation by using finite element and acoustic analysis software. The state space modelof the piezoelectric smart structure is identified by the proposed method, and the controlvoltage of the secondary actuator is computed by the linear quadratic optimal controlmethod. Finally, the sound pressure level of the structural acoustic radiation noise isdecreased by controlling the structure modals which have large coupling coefficientswith the acoustic modals.
引文
[1]C.R. Fu11er. Active control of transmission/radiation from elastic plates by vibrationinput: analysis. Journal of Sound and Vibration,1990,136:1-15.
[2]C.R. Fu11er. Optimal placement of piezoelectric actuators and polyvinylidenefluoride error sensor in active structural acoustic control approach. Journal of theAcoustical Society of America,1992,92(3):1521-1533.
[3]J.C. Burgress. Active adaptive sound control in a duct: a computer simulation.Journal of the Acoustical Society of America,1981,70(3):715-726.
[4]P.A. Nelson, S.J. Elliott. Active control of sound. London: Academic Press, P. A.Nelson,1992,20-128.
[5]A.T. Akhtar, M. Abe, M. Kawamata. Adaptive filtering with averaging-basedalgorithm for feedforward active noise control systems. IEEE Signal ProcessingLetters,2004,11(6):557-560.
[6]S.M. Kuo, X. Kong, W.S. Gan. Applications of adaptive feedback active noisecontrol system. IEEE Trans. on Control Systems Technology,2003,11(2):216-220.
[7]W.S. Gan, S. Mitra, S.M. Kuo. Adaptive feedback active noise control headset:implementation, evaluation and its extensions. IEEE Trans. on ConsumerElectronics,2005,51(3):975-982.
[8]M. Bodson, S.C. Douglas. Adaptive algorithms for the rejection of sinusoidaldisturbances with unknown frequency. Automatica,1997,33(12):2213-2221.
[9]S. Pigg, M. Bodson. Adaptive algorithms for the rejection of sinusoidal disturbancesacting on unknown plants. IEEE Trans. on Control Systems Technology,2010,18(4):822-836.
[10]D.R. Morgan, J.C. Thi. A delayless subband adaptive filter architecture. IEEE Trans.on Signal Processing,1995,3(8):1819-1830.
[11]J. Huo, S. Nordholm, Z. Zang. New weight transform schemes for delaylesssubband adaptive filter. Proceedings of GLOBECOM’01, New York, USA,2001,197-201.
[12]S.J. Park, J.H. Yun, Y.C. Park, et al. A delayless subband active noise control systemfor wideband noise control. IEEE Trans. on Speech and Audio Processing,2001,9(8):892-899.
[13]M. Wu, X.J, Qiu, G.Y. Chen. An overlap-save frequency domain implementation ofthe delayless subband ANC algorithm. IEEE Trans. on Audio, Speech, andLanguage Processing,2008,16(8):1706-1710.
[14]A.A. Milani, I.M.S. Panahi, P.C. Loizou. A new delayless subband adaptive filteringalgorithm for active noise control systems. IEEE Trans. on Audio, Speech, andLanguage Processing,2009,17(5):1038-1045.
[15]A.A. Milani,G. K. Issa, I.M.S. Panahi,et al. Analysis and optimal design of delaylesssubband active noise control systems for broadband noise. Signal Processing,2010,99:1153-1164.
[16] A.A. Milani, G. Kannan. On maximum achievable noise reduction in ANC systems.IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP),2010,349-352.
[17]A.A. Milani, G. Kannan, I.M. Panahi, et al. Weight stacking analysis of delaylesssubband adaptive algorithms for fMRI active noise cancellation. Proceeding of:Engineering in Medicine and Biology Workshop, Dallas,2007,134-137.
[18]S.D. Snyder, C.H. Hansen. The effect of transfer function estimation errors on thefiltered-X LMS Algorithm. IEEE Trans. on Signal Processing,1994,42(4):950-953.
[19]K. Fujii, M. Muneyasu, J. Ohga. Active noise control system using the simultaneousequation method without the estimation of error path filter coefficients. Electronicsand Communications in Japan (Part III: Fundamental Electronic Science),2002,85(12):101-108.
[20]D.Y. Zhou, V. Debrunner. A new active noise control algorithm that requires nosecondary path identification based on the SPR property. IEEE Trans. on SignalProcessing,2007,55(5):1719-1729.
[21]M. Wu, G. Chen, X.J. Qiu. An improved active noise control algorithm withoutsecondary path identification based on the frequency-domain subband architecture.IEEE Trans. on Audio, Speech and Language Processing,2008,16(8):1409-1419.
[22]H. Lan, M. Zhang, W. Ser. An active noise control system using onlinesecondary-path modeling with reduced auxiliary noise. IEEE Signal ProcessingLetters,2002,9(1):16-18.
[23]Y. Xiao, L. Ma, K. Khorasani. A robust narrowband active noise control system foraccommodating frequency mismatch. European Signal Processing Conference,Vienna, Austria,2004, Sep.6-10,917–920.
[24]Y. Xiao, L. Ma, K. Khorasani. A new robust narrowband active noise control systemin the presence of frequency mismatch. IEEE Trans. on Audio, Speech, andLanguage Processing,2006,14(6):2189-2200.
[25]Y. Xiao, R. K.Ward, L.Ma, et al. A new LMS-based Fourier analyzer in the presenceof frequency mismatch and applications. IEEE Trans. on Circuits and Systems I:Regular Papers,2005,52(1):230-245.
[26]Y. Xiao, L. Ma, R. K. Ward. Fast RLS Fourier analyzers capable of accommodatingfrequency mismatch. Signal Processing,2007,87(9):2197-2212.
[27]H.J. Jeon, T.G. Chang, S.Yu, et al. Analysis of frequency mismatch in narrowbandactive noise control. IEEE Trans. on Audio, Speech, and Language Processing,2010,18(6):1632-1642
[28]H.J. Jeon, T.G. Chang, S.M. Kuo. A narrowband active noise control system withfrequency corrector. IEEE Trans. on Audio, Speech, and Language Processing,2011,19(4):990-1002.
[29]J. Liu, Y.G. Xiao, J.W. Sun, et al. Analysis of online secondary-path modeling withauxiliary noise scaled by residual noise signal. IEEE Trans. on Audio, Speech, andLanguage Processing,2010,18(8):1978-1993.
[30]陈克安,尹雪飞.应用于多通道有源控制的自适应组合逆算法.信号处理,2006,22(3):366-369.
[31]陈克安.有源噪声控制.北京:国防工业出版社,2003,1-400.
[32]J.J. Shynk. Frequency domain and multirate adaptive filtering. IEEE SignalProcessing Magazine,1992,9(1):14-37.
[33]S.M. Kuo, P.K. Yenduri, A. Gupta. Frequency domain delayless active sound qualitycontrol algorithm. Journal of sound and vibration.2008,318:715-724.
[34]P. S. R. Diniz.自适应滤波算法与实现.北京:科学出版社.2007,41-360.
[35]陈克安,王进军,尹雪飞.次级通路特性及对有源控制算法的影响.振动工程学报,2005,18(4):480-485.
[36]陈克安,马远良.应用于有源消声的间歇自适应RLS算法.电子学报,1995,23(7):83-86.
[37]鲍雪山,徐新盛,吴英姿.一种新的具有信噪比处理增益的前馈型ANC系统设计.振动与冲击,2007,26(11):36-40.
[38]吴英姿,鲍雪山,徐新盛.应用频域ICA对参考输入进行预处理的ANC系统.振动与冲击,2008,27(8):81-84.
[39]李想,鲍雪山,王建国.基于抗串扰自适应噪声抵消器的参考输入预处理ANC系统.振动与冲击,2008,27(10):111-114.
[40] H.W. Kim, H.S. Park, S.K. Lee, et al. Modified-Filtered-U LMS algorithm foractive noise control and its application to a short acoustic duct. Mechanical Systemsand Signal Processing,2011,25(1):475-484.
[41] J. Park, S. Lee. A novel adaptive algorithm with an IIR filter and a variable stepsize for active noise control in a short duct. International Journal of AutomotiveTechnology,2012,13(2):223-229.
[42]S.M. Kuo, S. Mitra, W.S. Gan. Active noise control system for headphoneapplications. IEEE Trans. on Control Systems Technology,2006,14(2):331-335.
[43]R.Castae-Selga, R.S.S. Pea. Active noise hybrid time-varying control formotorcycle helmets. IEEE Trans. on Control Systems Technology,2010,18(3):602-612.
[44]R. Serizel, M. Moonen, J. Wouters, et al. Integrated active noise control and noisereduction in hearing aids. IEEE Trans. on Audio, Speech, and Language Processing,2010,18(6):1137-1146.
[45]A. Grewal, D.G. Zimcik, B. Leigh. Feedforward piezoelectric structural control: anapplication to aircraft cabin noise reduction. Journal of Aircraft,2001,38(1):164-173.
[46]R. Cabell, D. Palumbo, J. Vipperman. A principal component feedforward algorithmfor active noise control: flight test results. IEEE Trans. on Control SystemTechnology,2001,9(1):76-83
[47]S. Carra, M. Amabili, R. Ohayon, P.M. Hutin. Active vibration control of a thinrectangular plate in air or in contact with water in presence of tonal primarydisturbance. Aerospace Science and Technology,2008,12:54-61
[48]P. Belanger, A. Berry, Y. Pasco, et al. Multi-harmonic active structural acousticcontrol of a helicopter main transmission noise using the principal componentanalysis. Applied Acoustics,2009,70:153-164.
[49]陈仁文,孙亚飞,熊克,等.飞机座舱结构声的弹性波主动控制研究.南京航空航天大学学报,2003,35(5):489-493.
[50]陈仁文,孙亚飞,徐志伟.采用压电陶瓷的飞机座舱辐射噪声主动控制.压电与声光,2003,25(5):366-369.
[51]黄全振,朱晓锦,高守玮,等.在线实时辨识控制通道模型的振动主动控制.应用科学学报,2011,29(2):216-220.
[52]A. Sinha, C.K. Kao. Independent modal sliding mode control of vibration inflexible structures. Journal of Sound and Vibration,1991,147(2):352-358.
[53]M.S. Elbuni, M. Higashiguchi. Control of large flexible structures using poleplacement technique. Transactions of the Society of Instrument and ControlEngineers,1991,27(2):161-168.
[54]S. Hanagud, M.W. Obal, A.J. Calise. Optimal vibration control by the use ofpiezoceramic sensors and actuators. Journal of Guidance, Control, and Dynamics,1992,15(5):1199-1206.
[55]V. Balamurugan, S. Narayanan. Finite element modeling of stiffenedpiezolaminated plates and shells with piezoelectric layers for active vibration control.Smart Materials and Structures,2010,19(10):1-21.
[56]M. Marinaki, Y. Marinakis, G.E. Stavroulakis. Vibration control of beams withpiezoelectric sensors and actuators using particle swarm optimization. ExpertSystems with Applications,2011,38(6):6872-6883.
[57]K.R. Kumar, S. Narayanan. The optimal location of piezoelectric actuators andsensors for vibration control of plates. Smart Materials&Structures,2007,16(6):2680-2691.
[58]K.R. Kumar, S. Narayanan. Active vibration control of beams with optimalplacement of piezoelectric sensor/actuator pairs. Smart Materials&Structures,2008,17(5):1726-1731.
[59]T.N. Trajkov, J. Lefevre, U. Gabbert. Model-based active noise control of apiezoelectric structure. Mechanics,2007,26(2):71-77.
[60]T.N. Trajkov, J. Lefevre, S. Ringwelski, et al. Finite element and subspaceidentification approach to model development of a smart acoustic box withexperimental verification. Proceedings of World Academy of Science, Engineeringand Technology,2007,20:315-320.
[61]T. Nestorovic, H. Koppe, U. Gabbert. Subspace identification for the model basedcontroller design of a funnel-shaped structure. Mechanics, Automatic Control andRobbtics,2005,4(17):257-263.
[62]T.N. Trajkov, H. Koppe, U. Gabbert. Active vibration control using optimal LQtracking system with additional dynamics. International Journal of control,2005,78(15):1182-1197.
[63]U. Gabbert, T.N. Trajkov, H. Koppe. Modelling, Control and simulation ofpiezoelectric smart structures using finite element method and optimal LQ control.Mechanics, Automatic Control and Robotics,2002,3(12):417-430.
[64]T.N. Trajkov, F. Seeger, H. Koppe, et al. Optimal LQ controller with additionaldynamics for the active vibration suppression of a car roof. Mechanics, AutomaticControl and Robotics,2006,5(1):117-129.
[65]X.J. Dong, G. Meng, J.C. Peng. Vibration control of piezoelectric smart structuresbased on system identification technique: numerical simulation and experimentalstudy. Journal of Sound and Vibration,2006,297:680-693.
[66]董兴建,刘龙,孟光,等.基于有限元和系统辨识的智能结构主动控制.振动与工程学报,2005,18(3):329-324.
[67]L. Ljung. System Identification: Theory for Users.2nd Edition. Prentice Hall, NewJersey,1999,123-467.
[68]P. Frank Pai, A.N. Palazotto. Detection and identification of nonlinearities byamplitude and frequency modulation analysis. Mechanical Systems and SignalProcessing,2008,22(5):1107-1132.
[69]Z.Y. Shi, S.S. Law. Identification of linear time-varying dynamical systems usingHilbert transform and EMD method. Journal of Applied Mechanics,2006,74(2):223-230.
[70]T.J.J. Van Den Boom, B.R.J Haverkamp. Towards a state-space polytopicuncertainty description using subspace model identification techniques. InternationalJournal of Control,2003,76(15):1570-1583.
[71]P.M.J Van Den Hof, R.J.P. Schrama. Identification for control: closed-loop issues.Automatica,1995,31(12):1751-1770.
[72]P.V. Overschee, B.D. Moor N4SID: Subspace algorithms for the identification ofcombined deterministic-stochastic systems. Automatica,1994,30(1):75-93.
[73]T. Katayama. Subspace methods for system identification. London: Springer,2005.
[74]T. Katayama, H. Kawauchi, G.Picci. Subspace identification of closed-loop systemsby orthogonal projection method. Automatica,2005,41(5):863-872.
[75]M.Verhaegen. Application of a subspace model identification technique to identifyLTI systems operating in closed-loop. Automatica,1993,29(4):1027-1040.
[76]M. Lovera, T. Gustafsson, M.Verhaegen. Recursive subspace identification of linearand non-linear Wiener state-space models. Automatica,2000,36(11):1639-1650.
[77]G. Mercere, S. Lecoeuche, M. Lovera. Recursive subspace identification based oninstrumental variable unconstrained quadratic optimization. International Journal ofAdaptive Control and Signal Processing,2004,18:771-797.
[78]G. Mercere, L. Bako, S. Lecoeuche. Propagator-based methods for recursivesubspace model identification. Signal Processing,2008,88:468-491.
[79]G. Mercere, M. Lovera. Convergence analysis of instrumental variable recursivesubspace identification algorithm. Automation,2007,43:1377-1386.
[80]庞世伟,于开平,邹经湘.用于时变结构模态参数识别的投影估计递推子空间方法.工程力学,2005,22(5):115-119.
[81]杨利芳,于开平,庞世伟,等.用于线性时变结构系统辨识的子空间方法比较研究.振动与冲击,2007,26(3):8-12.
[82]于开平,庞世伟,赵婕.时变线性/非线性结构参数识别及系统辨识方法研究进展.科学通报,2009,54(20):3147-3156.
[83]杨华,李少远.一种新的基于遗忘因子的递推子空间辨识算法.控制理论与应用,2009,26(1):69-72.
[84]李少远,吴永玲.时变遗忘因子的子空间辨识及其预测控制器设计.同济大学学报,2010,38(11):1651-1655.
[85]张家滨,陈国平.基于随机子空间的递推在线模态辨识算法.振动与冲击,2009,28(8):42-45.
[86]H. Oku, H. Kimura. Recursive4SID algorithm using gradient type subspacetracking, Automatica,2002,38(11):1035-1043.
[87]王新,吕剑虹,向文国.一种改进的子空间辨识方法在热工过程中的应用.中国电机工程学报,2010,30(32):96-102.
[88]李幼凤,苏宏业,褚健.工业过程的子空间模型辨识.控制理论与应用,2007,24(5):803-806.
[89]S.M. Kuo, D.R. Morgan. Active noise control: a tutorial review. Proceedings of theIEEE,1999,87(6):943-973.
[90]M.R. Bai, Y. Lin, J. Lai. Reduction of electronic delay in active noise controlsystems-A multirate signal processing approach. Journal of the Acoustical Societyof America,2002,111(2):916-924.
[91]E. Anderson, A.B. Wright. Study of causal component placement in an active soundcancellation system. Journal of the Arkansas Academy of Science,2006,60:20-26.
[92]X. Kong, S.M. Kuo. Study of causality constraint on feedforward active noisecontrol system. IEEE Trans. on Circuits and System-II: Analog and Digtal SignalProcessing,1999,46(2):183-186.
[93]喻迪,邹海山,邱小军.不满足因果性的前馈有源噪声控制系统性能研究.声学技术,2011,30(6)205-206.
[94]F. Emmanuel. Infinite non-causality in active cancellation of random noise. Journalof sound and vibration,2006,290:597-618.
[95]G. Mcgovern. A model for room acoustics. http://www.sgm-audio.com/research/rir/rir.html
[96]杨德森,陈欢,时胜国.基于MUSIC算法的水下噪声源近场高分辨定位方法.哈尔滨工程大学学报,2011,32(8):1019-1023.
[97]M. Wax, T. Shan, T. Kailath, Spatio-temporal spectral analysis by eigenstructuremethods. IEEE Trans. on Acoustics, Speech and Signal Processing,1984,ASSP-32(4):817-827.
[98]H. Huang, M. Kaveh. Focussing matrices for coherent signal subspace processing.IEEE Trans. on Acoustic. Speech and Signal Processing,1998,36(8):1272-1281.
[99]S. Valaee, P. Kabal. Wideband array processing using a two-sided correlationtransformation. IEEE Trans. on Signal Processing,1995,43(1):160-172.
[100]Y.S. Yoon, L.M. Kaplan, J.H. Meclellan. TOPS: new DOA estimator for widebandsignals. IEEE Trans. on Signal Processing,2006,54(6):1977-1989.
[101]X.S. Guo, Q. Wan, C.Q. Chang, et al. Source localization using a sparserepresentation framework to achieve super resolution. Multidimensional Systemsand Signal Processing,2010,21(4):391-402.
[102]X. Xu, X.H. Wei, Z.F. Ye. DOA estimation based on sparse signal recoveryutilizing weight1-norm penalty. IEEE Signal Processing Letters,2012,19(3):155-158.
[103]I.F. Gorodnitsky, B.D. Rao. Sparse signal reconstruction form limited data usingFOCUSS: a re-weighted minimum norm algorithm. IEEE Trans. on signalprocessing,1997,45(3):600-616.
[104]J.H. Yin, T.Q. Chen. Direction-of-arrival estimation using a sparse representationof array covariance vectors. IEEE Trans. on Signal Processing,2011,59(9):4489-4493.
[105]M.M. Hyder, K. Mahata. Direction-of-arrival estimation using a mixed2,0normapproximation. IEEE Trans. on signal processing,2010,58(9):4646-4655.
[106]D. Needell, R. Vershynin. Signal recovery from incomplete and inaccuratemeasurements via regularized orthogonal matching pursuit. IEEE Journal ofSelected Topics in Signal Processing,2010,4(2):310-316.
[107]D. Needle, J.A. Tropp. CoSaMP: iterative signal recovery from incomplete andinaccurate samples. Applied and Computational Harmonic Analysis,2009,26(3):301-321.
[108]H. Mohimani, M. Babaie-zadeh, C. Jutten. A fast approach for overcompletesparse decomposition based on smoothed0norm. IEEE Trans. on SignalProcessing,2009,57(1):289-301.
[109]D. Malioutov. A sparse reconstruction perspective for source localization withsensor arrays. Master's thesis. Massachusetts Institute of Technology, Arizona,USA,2003.
[110]D. Malioutov, M. Cetin, A.S.Willsky. A sparse signal reconstruction perspectivefor source localization with sensor array. IEEE Trans. on signal processing,2005,53(8):3010-3022.
[111]Z.J. Tang, G. Blacquiere, G. Leus. Aliasing-free wideband beamforming usingsparse signal representation. IEEE Trans. on signal processing,2011,59(7):3464-3469.
[112]Z.M. Liu, Z.T. Huang, Y.Y. Zhou. Direction-of-arrival estimation of widebandsignals via covariance matrix sparse representation. IEEE Trans. on signal process-ing,2011,59(9):4256-4270.
[113]A.B. Gershman, J. Bohme. A note on most favorable array geometries for DOAestimation and array interpolation. Signal Processing Letters,1997,4(8):232-235.
[114]丁卫安,马远良.虚拟阵列变换法解相干信号MUSIC算法研究.微波学报,2008,24(2):27-30.
[115]P. Chevalier, A. Ferreol, L. Albera. High resolution direction finding from higherorder statistics: The-MUSIC Algorithm. IEEE Trans. on Signal Processing,2006,54(8):2986-2997.
[116]L. De Lathauwer, J. Castiaing, J.F. Cardoso. Fourth order cumulant-based blindidentification of undetermined mixtures. IEEE Trans. on Signal Processing,2007,55(6):2965-2973.
[117]E. Grosicki, K. Abed-Meraim,Y. Hua. A weighted linear prediction method fornear-field source localization. IEEE Trans. on Signal Processing,2005,53(10):3651-3660.
[118]W.K. Ma, T.H. Hsieh, C.Y. Chi. DOA estimation of quasi-stationary signals withless sensors than sources and unknown spatial noise covariance: a Khatri-Raosubspace approach. IEEE Trans. on Signal Processing,2010,58(4):2168-2180.
[119]D.H. Feng, M. Bao, Z.F. Ye, et al. A novel wideband DOA estimator based onKhatri-Rao subspace approach. Signal Processing.2011,91:2415-2419.
[120]潘捷,周建江.基于凸优化的Khatri-Rao子空间宽带DOA估计.电子与信息学报,2013,35(1):80-84.
[121]B.W. K. Ling, K.Z.K.A. Hosni, H.H. Dam. Designs of low delay cosine modulatedfilter banks and subband amplifiers,19thEuropean Signal Processing Conference,EUSIPCO, Barcelona,2011,8.29-9.2.
[122]Z.P. Lin, Y.Z. Liu. Design of arbitrary complex coefficient WLS FIR filters withgroup delay constraints. IEEE Trans. on Signal Processing,2009,57(8):3274-3279.
[123]K.D. gancay, O. Tanr kulu. Adaptive filtering algorithms with selective partialupdate. IEEE Trans. on Circuits and System-II: Analog and Digital SignalProcessing,2001,48(8):762-769.
[124]H.H. Dam, S. Nordholm, A.Cantoni,et al. Iterative method for the design of DFTfilter bank. IEEE Trans. on Circuits and Systems II: express briefs,2004,51(11):581-586.
[125]J.Z. Jiang, P.L. Shui, Z.J. Zhang. Design of oversampled DFT-modulated filterbanks via modified Newton’s method. IET Signal Processing,2011,5(3):271-280.
[126]Z.J. Zhan. Efficient design of cosine modulated filter banks based on gradientinformation. IEEE Signal Processing Letters,2007,14(12):940-943.
[127]肖玮,涂亚庆,何丽. DTFT频谱细化特性分析及其快速算法设计.电子与信息学报,2011,33(6):1395-1400.
[128]曾小东,曾德国,张文超,等.基于OMP-SVD的多分量单频信号频率估计.雷达科学与技术,2011,9(2):188-191.
[129]P. Stoica, A. Eriksson. MUSIC estimation of real-valued sine-wave frequencies.Signal Processing,1995,42:139-146.
[130]K. Mahata, T. Soderstrom. ESPRIT-like estimation of real-valued sinusoidalfrequencies. IEEE Trans. on Signal Processing,2004,52(5):1161-1170.
[131]C.Y. Chang, S.M. Kuo. Complete direct/parallel structure for narrowband activenoise control systems. IET Signal Processing,2013,7(6):477-485.
[132]R. Roy, T. Kailath. ESPRIT–Estimation of signal parameters via rotationalinvariance techniques. IEEE Trans. Acoust, Speech Signal Processing,1989,ASSP-37(7):984-995.
[133]Y.B. Hua, X. Yong, T.P. Chen,et al. A New Look at the Power Method for FastSubspace Tracking. Digital Signal Processing,1999,9:297-314.
[134]]R. Badeau, B. David, G. Richard. Fast approximated power iteration subspacetracking. IEEE Trans. on signal processing,2005,53(8):2931-2941.
[135]P.V. Overschee, B.D. Moor. Subspace identification for linear systems: theory,implementation and applications. Kluwer Academic Publishers Dordrecht,Netherlands,1996.
[136]K. Kameyama, A. Ohsumi., Y. Matsuura, et al. Recursive4SID-basedidentification algorithm with fixed input-output data size. International Journal ofInnovative Computing, Information and Control,2005,1(3):17-33.
[137]B. Yang. Projeciton approximation subspace tracking. IEEE Trans. on SignalProcessing,1995,43(1):95-107.
[138]K.A. Meraim, A. Chkeif, Y. Hua. Fast orthonormal PAST algorithm. IEEE SignalProcessing Letters,2000,7(3):60-62.
[139]A. Valizadelh, M. Karimi. Fast subspace tracking algorithm based on theconstrained projection approximation. EURASIP Journal on Advances in SignalProcessing,2009, Article ID576972.16pages.
[140]B. Yang. An extension of the PASTd algorithm to both rank and subspace trackingIEEE Signal Processing Letters,1995,2(9):179-182.
[141]周星德,姜冬菊.结构振动主动控制.北京:科学出版社,2009,46-121.
[142]P. Li, I. Postlethwaite, M. Turner. Subsapce-based system identification forhelicopter dynamic modeling, American Helicopter Society63rd Annual Forum,Virginia Beach, VA, May1-3,2007.
[143]张贤达.矩阵分析与应用.北京:清华大学出版社,2006,89-234.
[144]李增刚. SYSNOISE Rev5.6详解.北京:国防工业出版社,2005,21-315.
[145]靳国永,杨铁军,刘志刚.弹性板封闭声腔结构-声耦合特性分析.声学学报,2007,32(2):85-96.
[2]C.R. Fu11er. Optimal placement of piezoelectric actuators and polyvinylidenefluoride error sensor in active structural acoustic control approach. Journal of theAcoustical Society of America,1992,92(3):1521-1533.
[3]J.C. Burgress. Active adaptive sound control in a duct: a computer simulation.Journal of the Acoustical Society of America,1981,70(3):715-726.
[4]P.A. Nelson, S.J. Elliott. Active control of sound. London: Academic Press, P. A.Nelson,1992,20-128.
[5]A.T. Akhtar, M. Abe, M. Kawamata. Adaptive filtering with averaging-basedalgorithm for feedforward active noise control systems. IEEE Signal ProcessingLetters,2004,11(6):557-560.
[6]S.M. Kuo, X. Kong, W.S. Gan. Applications of adaptive feedback active noisecontrol system. IEEE Trans. on Control Systems Technology,2003,11(2):216-220.
[7]W.S. Gan, S. Mitra, S.M. Kuo. Adaptive feedback active noise control headset:implementation, evaluation and its extensions. IEEE Trans. on ConsumerElectronics,2005,51(3):975-982.
[8]M. Bodson, S.C. Douglas. Adaptive algorithms for the rejection of sinusoidaldisturbances with unknown frequency. Automatica,1997,33(12):2213-2221.
[9]S. Pigg, M. Bodson. Adaptive algorithms for the rejection of sinusoidal disturbancesacting on unknown plants. IEEE Trans. on Control Systems Technology,2010,18(4):822-836.
[10]D.R. Morgan, J.C. Thi. A delayless subband adaptive filter architecture. IEEE Trans.on Signal Processing,1995,3(8):1819-1830.
[11]J. Huo, S. Nordholm, Z. Zang. New weight transform schemes for delaylesssubband adaptive filter. Proceedings of GLOBECOM’01, New York, USA,2001,197-201.
[12]S.J. Park, J.H. Yun, Y.C. Park, et al. A delayless subband active noise control systemfor wideband noise control. IEEE Trans. on Speech and Audio Processing,2001,9(8):892-899.
[13]M. Wu, X.J, Qiu, G.Y. Chen. An overlap-save frequency domain implementation ofthe delayless subband ANC algorithm. IEEE Trans. on Audio, Speech, andLanguage Processing,2008,16(8):1706-1710.
[14]A.A. Milani, I.M.S. Panahi, P.C. Loizou. A new delayless subband adaptive filteringalgorithm for active noise control systems. IEEE Trans. on Audio, Speech, andLanguage Processing,2009,17(5):1038-1045.
[15]A.A. Milani,G. K. Issa, I.M.S. Panahi,et al. Analysis and optimal design of delaylesssubband active noise control systems for broadband noise. Signal Processing,2010,99:1153-1164.
[16] A.A. Milani, G. Kannan. On maximum achievable noise reduction in ANC systems.IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP),2010,349-352.
[17]A.A. Milani, G. Kannan, I.M. Panahi, et al. Weight stacking analysis of delaylesssubband adaptive algorithms for fMRI active noise cancellation. Proceeding of:Engineering in Medicine and Biology Workshop, Dallas,2007,134-137.
[18]S.D. Snyder, C.H. Hansen. The effect of transfer function estimation errors on thefiltered-X LMS Algorithm. IEEE Trans. on Signal Processing,1994,42(4):950-953.
[19]K. Fujii, M. Muneyasu, J. Ohga. Active noise control system using the simultaneousequation method without the estimation of error path filter coefficients. Electronicsand Communications in Japan (Part III: Fundamental Electronic Science),2002,85(12):101-108.
[20]D.Y. Zhou, V. Debrunner. A new active noise control algorithm that requires nosecondary path identification based on the SPR property. IEEE Trans. on SignalProcessing,2007,55(5):1719-1729.
[21]M. Wu, G. Chen, X.J. Qiu. An improved active noise control algorithm withoutsecondary path identification based on the frequency-domain subband architecture.IEEE Trans. on Audio, Speech and Language Processing,2008,16(8):1409-1419.
[22]H. Lan, M. Zhang, W. Ser. An active noise control system using onlinesecondary-path modeling with reduced auxiliary noise. IEEE Signal ProcessingLetters,2002,9(1):16-18.
[23]Y. Xiao, L. Ma, K. Khorasani. A robust narrowband active noise control system foraccommodating frequency mismatch. European Signal Processing Conference,Vienna, Austria,2004, Sep.6-10,917–920.
[24]Y. Xiao, L. Ma, K. Khorasani. A new robust narrowband active noise control systemin the presence of frequency mismatch. IEEE Trans. on Audio, Speech, andLanguage Processing,2006,14(6):2189-2200.
[25]Y. Xiao, R. K.Ward, L.Ma, et al. A new LMS-based Fourier analyzer in the presenceof frequency mismatch and applications. IEEE Trans. on Circuits and Systems I:Regular Papers,2005,52(1):230-245.
[26]Y. Xiao, L. Ma, R. K. Ward. Fast RLS Fourier analyzers capable of accommodatingfrequency mismatch. Signal Processing,2007,87(9):2197-2212.
[27]H.J. Jeon, T.G. Chang, S.Yu, et al. Analysis of frequency mismatch in narrowbandactive noise control. IEEE Trans. on Audio, Speech, and Language Processing,2010,18(6):1632-1642
[28]H.J. Jeon, T.G. Chang, S.M. Kuo. A narrowband active noise control system withfrequency corrector. IEEE Trans. on Audio, Speech, and Language Processing,2011,19(4):990-1002.
[29]J. Liu, Y.G. Xiao, J.W. Sun, et al. Analysis of online secondary-path modeling withauxiliary noise scaled by residual noise signal. IEEE Trans. on Audio, Speech, andLanguage Processing,2010,18(8):1978-1993.
[30]陈克安,尹雪飞.应用于多通道有源控制的自适应组合逆算法.信号处理,2006,22(3):366-369.
[31]陈克安.有源噪声控制.北京:国防工业出版社,2003,1-400.
[32]J.J. Shynk. Frequency domain and multirate adaptive filtering. IEEE SignalProcessing Magazine,1992,9(1):14-37.
[33]S.M. Kuo, P.K. Yenduri, A. Gupta. Frequency domain delayless active sound qualitycontrol algorithm. Journal of sound and vibration.2008,318:715-724.
[34]P. S. R. Diniz.自适应滤波算法与实现.北京:科学出版社.2007,41-360.
[35]陈克安,王进军,尹雪飞.次级通路特性及对有源控制算法的影响.振动工程学报,2005,18(4):480-485.
[36]陈克安,马远良.应用于有源消声的间歇自适应RLS算法.电子学报,1995,23(7):83-86.
[37]鲍雪山,徐新盛,吴英姿.一种新的具有信噪比处理增益的前馈型ANC系统设计.振动与冲击,2007,26(11):36-40.
[38]吴英姿,鲍雪山,徐新盛.应用频域ICA对参考输入进行预处理的ANC系统.振动与冲击,2008,27(8):81-84.
[39]李想,鲍雪山,王建国.基于抗串扰自适应噪声抵消器的参考输入预处理ANC系统.振动与冲击,2008,27(10):111-114.
[40] H.W. Kim, H.S. Park, S.K. Lee, et al. Modified-Filtered-U LMS algorithm foractive noise control and its application to a short acoustic duct. Mechanical Systemsand Signal Processing,2011,25(1):475-484.
[41] J. Park, S. Lee. A novel adaptive algorithm with an IIR filter and a variable stepsize for active noise control in a short duct. International Journal of AutomotiveTechnology,2012,13(2):223-229.
[42]S.M. Kuo, S. Mitra, W.S. Gan. Active noise control system for headphoneapplications. IEEE Trans. on Control Systems Technology,2006,14(2):331-335.
[43]R.Castae-Selga, R.S.S. Pea. Active noise hybrid time-varying control formotorcycle helmets. IEEE Trans. on Control Systems Technology,2010,18(3):602-612.
[44]R. Serizel, M. Moonen, J. Wouters, et al. Integrated active noise control and noisereduction in hearing aids. IEEE Trans. on Audio, Speech, and Language Processing,2010,18(6):1137-1146.
[45]A. Grewal, D.G. Zimcik, B. Leigh. Feedforward piezoelectric structural control: anapplication to aircraft cabin noise reduction. Journal of Aircraft,2001,38(1):164-173.
[46]R. Cabell, D. Palumbo, J. Vipperman. A principal component feedforward algorithmfor active noise control: flight test results. IEEE Trans. on Control SystemTechnology,2001,9(1):76-83
[47]S. Carra, M. Amabili, R. Ohayon, P.M. Hutin. Active vibration control of a thinrectangular plate in air or in contact with water in presence of tonal primarydisturbance. Aerospace Science and Technology,2008,12:54-61
[48]P. Belanger, A. Berry, Y. Pasco, et al. Multi-harmonic active structural acousticcontrol of a helicopter main transmission noise using the principal componentanalysis. Applied Acoustics,2009,70:153-164.
[49]陈仁文,孙亚飞,熊克,等.飞机座舱结构声的弹性波主动控制研究.南京航空航天大学学报,2003,35(5):489-493.
[50]陈仁文,孙亚飞,徐志伟.采用压电陶瓷的飞机座舱辐射噪声主动控制.压电与声光,2003,25(5):366-369.
[51]黄全振,朱晓锦,高守玮,等.在线实时辨识控制通道模型的振动主动控制.应用科学学报,2011,29(2):216-220.
[52]A. Sinha, C.K. Kao. Independent modal sliding mode control of vibration inflexible structures. Journal of Sound and Vibration,1991,147(2):352-358.
[53]M.S. Elbuni, M. Higashiguchi. Control of large flexible structures using poleplacement technique. Transactions of the Society of Instrument and ControlEngineers,1991,27(2):161-168.
[54]S. Hanagud, M.W. Obal, A.J. Calise. Optimal vibration control by the use ofpiezoceramic sensors and actuators. Journal of Guidance, Control, and Dynamics,1992,15(5):1199-1206.
[55]V. Balamurugan, S. Narayanan. Finite element modeling of stiffenedpiezolaminated plates and shells with piezoelectric layers for active vibration control.Smart Materials and Structures,2010,19(10):1-21.
[56]M. Marinaki, Y. Marinakis, G.E. Stavroulakis. Vibration control of beams withpiezoelectric sensors and actuators using particle swarm optimization. ExpertSystems with Applications,2011,38(6):6872-6883.
[57]K.R. Kumar, S. Narayanan. The optimal location of piezoelectric actuators andsensors for vibration control of plates. Smart Materials&Structures,2007,16(6):2680-2691.
[58]K.R. Kumar, S. Narayanan. Active vibration control of beams with optimalplacement of piezoelectric sensor/actuator pairs. Smart Materials&Structures,2008,17(5):1726-1731.
[59]T.N. Trajkov, J. Lefevre, U. Gabbert. Model-based active noise control of apiezoelectric structure. Mechanics,2007,26(2):71-77.
[60]T.N. Trajkov, J. Lefevre, S. Ringwelski, et al. Finite element and subspaceidentification approach to model development of a smart acoustic box withexperimental verification. Proceedings of World Academy of Science, Engineeringand Technology,2007,20:315-320.
[61]T. Nestorovic, H. Koppe, U. Gabbert. Subspace identification for the model basedcontroller design of a funnel-shaped structure. Mechanics, Automatic Control andRobbtics,2005,4(17):257-263.
[62]T.N. Trajkov, H. Koppe, U. Gabbert. Active vibration control using optimal LQtracking system with additional dynamics. International Journal of control,2005,78(15):1182-1197.
[63]U. Gabbert, T.N. Trajkov, H. Koppe. Modelling, Control and simulation ofpiezoelectric smart structures using finite element method and optimal LQ control.Mechanics, Automatic Control and Robotics,2002,3(12):417-430.
[64]T.N. Trajkov, F. Seeger, H. Koppe, et al. Optimal LQ controller with additionaldynamics for the active vibration suppression of a car roof. Mechanics, AutomaticControl and Robotics,2006,5(1):117-129.
[65]X.J. Dong, G. Meng, J.C. Peng. Vibration control of piezoelectric smart structuresbased on system identification technique: numerical simulation and experimentalstudy. Journal of Sound and Vibration,2006,297:680-693.
[66]董兴建,刘龙,孟光,等.基于有限元和系统辨识的智能结构主动控制.振动与工程学报,2005,18(3):329-324.
[67]L. Ljung. System Identification: Theory for Users.2nd Edition. Prentice Hall, NewJersey,1999,123-467.
[68]P. Frank Pai, A.N. Palazotto. Detection and identification of nonlinearities byamplitude and frequency modulation analysis. Mechanical Systems and SignalProcessing,2008,22(5):1107-1132.
[69]Z.Y. Shi, S.S. Law. Identification of linear time-varying dynamical systems usingHilbert transform and EMD method. Journal of Applied Mechanics,2006,74(2):223-230.
[70]T.J.J. Van Den Boom, B.R.J Haverkamp. Towards a state-space polytopicuncertainty description using subspace model identification techniques. InternationalJournal of Control,2003,76(15):1570-1583.
[71]P.M.J Van Den Hof, R.J.P. Schrama. Identification for control: closed-loop issues.Automatica,1995,31(12):1751-1770.
[72]P.V. Overschee, B.D. Moor N4SID: Subspace algorithms for the identification ofcombined deterministic-stochastic systems. Automatica,1994,30(1):75-93.
[73]T. Katayama. Subspace methods for system identification. London: Springer,2005.
[74]T. Katayama, H. Kawauchi, G.Picci. Subspace identification of closed-loop systemsby orthogonal projection method. Automatica,2005,41(5):863-872.
[75]M.Verhaegen. Application of a subspace model identification technique to identifyLTI systems operating in closed-loop. Automatica,1993,29(4):1027-1040.
[76]M. Lovera, T. Gustafsson, M.Verhaegen. Recursive subspace identification of linearand non-linear Wiener state-space models. Automatica,2000,36(11):1639-1650.
[77]G. Mercere, S. Lecoeuche, M. Lovera. Recursive subspace identification based oninstrumental variable unconstrained quadratic optimization. International Journal ofAdaptive Control and Signal Processing,2004,18:771-797.
[78]G. Mercere, L. Bako, S. Lecoeuche. Propagator-based methods for recursivesubspace model identification. Signal Processing,2008,88:468-491.
[79]G. Mercere, M. Lovera. Convergence analysis of instrumental variable recursivesubspace identification algorithm. Automation,2007,43:1377-1386.
[80]庞世伟,于开平,邹经湘.用于时变结构模态参数识别的投影估计递推子空间方法.工程力学,2005,22(5):115-119.
[81]杨利芳,于开平,庞世伟,等.用于线性时变结构系统辨识的子空间方法比较研究.振动与冲击,2007,26(3):8-12.
[82]于开平,庞世伟,赵婕.时变线性/非线性结构参数识别及系统辨识方法研究进展.科学通报,2009,54(20):3147-3156.
[83]杨华,李少远.一种新的基于遗忘因子的递推子空间辨识算法.控制理论与应用,2009,26(1):69-72.
[84]李少远,吴永玲.时变遗忘因子的子空间辨识及其预测控制器设计.同济大学学报,2010,38(11):1651-1655.
[85]张家滨,陈国平.基于随机子空间的递推在线模态辨识算法.振动与冲击,2009,28(8):42-45.
[86]H. Oku, H. Kimura. Recursive4SID algorithm using gradient type subspacetracking, Automatica,2002,38(11):1035-1043.
[87]王新,吕剑虹,向文国.一种改进的子空间辨识方法在热工过程中的应用.中国电机工程学报,2010,30(32):96-102.
[88]李幼凤,苏宏业,褚健.工业过程的子空间模型辨识.控制理论与应用,2007,24(5):803-806.
[89]S.M. Kuo, D.R. Morgan. Active noise control: a tutorial review. Proceedings of theIEEE,1999,87(6):943-973.
[90]M.R. Bai, Y. Lin, J. Lai. Reduction of electronic delay in active noise controlsystems-A multirate signal processing approach. Journal of the Acoustical Societyof America,2002,111(2):916-924.
[91]E. Anderson, A.B. Wright. Study of causal component placement in an active soundcancellation system. Journal of the Arkansas Academy of Science,2006,60:20-26.
[92]X. Kong, S.M. Kuo. Study of causality constraint on feedforward active noisecontrol system. IEEE Trans. on Circuits and System-II: Analog and Digtal SignalProcessing,1999,46(2):183-186.
[93]喻迪,邹海山,邱小军.不满足因果性的前馈有源噪声控制系统性能研究.声学技术,2011,30(6)205-206.
[94]F. Emmanuel. Infinite non-causality in active cancellation of random noise. Journalof sound and vibration,2006,290:597-618.
[95]G. Mcgovern. A model for room acoustics. http://www.sgm-audio.com/research/rir/rir.html
[96]杨德森,陈欢,时胜国.基于MUSIC算法的水下噪声源近场高分辨定位方法.哈尔滨工程大学学报,2011,32(8):1019-1023.
[97]M. Wax, T. Shan, T. Kailath, Spatio-temporal spectral analysis by eigenstructuremethods. IEEE Trans. on Acoustics, Speech and Signal Processing,1984,ASSP-32(4):817-827.
[98]H. Huang, M. Kaveh. Focussing matrices for coherent signal subspace processing.IEEE Trans. on Acoustic. Speech and Signal Processing,1998,36(8):1272-1281.
[99]S. Valaee, P. Kabal. Wideband array processing using a two-sided correlationtransformation. IEEE Trans. on Signal Processing,1995,43(1):160-172.
[100]Y.S. Yoon, L.M. Kaplan, J.H. Meclellan. TOPS: new DOA estimator for widebandsignals. IEEE Trans. on Signal Processing,2006,54(6):1977-1989.
[101]X.S. Guo, Q. Wan, C.Q. Chang, et al. Source localization using a sparserepresentation framework to achieve super resolution. Multidimensional Systemsand Signal Processing,2010,21(4):391-402.
[102]X. Xu, X.H. Wei, Z.F. Ye. DOA estimation based on sparse signal recoveryutilizing weight1-norm penalty. IEEE Signal Processing Letters,2012,19(3):155-158.
[103]I.F. Gorodnitsky, B.D. Rao. Sparse signal reconstruction form limited data usingFOCUSS: a re-weighted minimum norm algorithm. IEEE Trans. on signalprocessing,1997,45(3):600-616.
[104]J.H. Yin, T.Q. Chen. Direction-of-arrival estimation using a sparse representationof array covariance vectors. IEEE Trans. on Signal Processing,2011,59(9):4489-4493.
[105]M.M. Hyder, K. Mahata. Direction-of-arrival estimation using a mixed2,0normapproximation. IEEE Trans. on signal processing,2010,58(9):4646-4655.
[106]D. Needell, R. Vershynin. Signal recovery from incomplete and inaccuratemeasurements via regularized orthogonal matching pursuit. IEEE Journal ofSelected Topics in Signal Processing,2010,4(2):310-316.
[107]D. Needle, J.A. Tropp. CoSaMP: iterative signal recovery from incomplete andinaccurate samples. Applied and Computational Harmonic Analysis,2009,26(3):301-321.
[108]H. Mohimani, M. Babaie-zadeh, C. Jutten. A fast approach for overcompletesparse decomposition based on smoothed0norm. IEEE Trans. on SignalProcessing,2009,57(1):289-301.
[109]D. Malioutov. A sparse reconstruction perspective for source localization withsensor arrays. Master's thesis. Massachusetts Institute of Technology, Arizona,USA,2003.
[110]D. Malioutov, M. Cetin, A.S.Willsky. A sparse signal reconstruction perspectivefor source localization with sensor array. IEEE Trans. on signal processing,2005,53(8):3010-3022.
[111]Z.J. Tang, G. Blacquiere, G. Leus. Aliasing-free wideband beamforming usingsparse signal representation. IEEE Trans. on signal processing,2011,59(7):3464-3469.
[112]Z.M. Liu, Z.T. Huang, Y.Y. Zhou. Direction-of-arrival estimation of widebandsignals via covariance matrix sparse representation. IEEE Trans. on signal process-ing,2011,59(9):4256-4270.
[113]A.B. Gershman, J. Bohme. A note on most favorable array geometries for DOAestimation and array interpolation. Signal Processing Letters,1997,4(8):232-235.
[114]丁卫安,马远良.虚拟阵列变换法解相干信号MUSIC算法研究.微波学报,2008,24(2):27-30.
[115]P. Chevalier, A. Ferreol, L. Albera. High resolution direction finding from higherorder statistics: The-MUSIC Algorithm. IEEE Trans. on Signal Processing,2006,54(8):2986-2997.
[116]L. De Lathauwer, J. Castiaing, J.F. Cardoso. Fourth order cumulant-based blindidentification of undetermined mixtures. IEEE Trans. on Signal Processing,2007,55(6):2965-2973.
[117]E. Grosicki, K. Abed-Meraim,Y. Hua. A weighted linear prediction method fornear-field source localization. IEEE Trans. on Signal Processing,2005,53(10):3651-3660.
[118]W.K. Ma, T.H. Hsieh, C.Y. Chi. DOA estimation of quasi-stationary signals withless sensors than sources and unknown spatial noise covariance: a Khatri-Raosubspace approach. IEEE Trans. on Signal Processing,2010,58(4):2168-2180.
[119]D.H. Feng, M. Bao, Z.F. Ye, et al. A novel wideband DOA estimator based onKhatri-Rao subspace approach. Signal Processing.2011,91:2415-2419.
[120]潘捷,周建江.基于凸优化的Khatri-Rao子空间宽带DOA估计.电子与信息学报,2013,35(1):80-84.
[121]B.W. K. Ling, K.Z.K.A. Hosni, H.H. Dam. Designs of low delay cosine modulatedfilter banks and subband amplifiers,19thEuropean Signal Processing Conference,EUSIPCO, Barcelona,2011,8.29-9.2.
[122]Z.P. Lin, Y.Z. Liu. Design of arbitrary complex coefficient WLS FIR filters withgroup delay constraints. IEEE Trans. on Signal Processing,2009,57(8):3274-3279.
[123]K.D. gancay, O. Tanr kulu. Adaptive filtering algorithms with selective partialupdate. IEEE Trans. on Circuits and System-II: Analog and Digital SignalProcessing,2001,48(8):762-769.
[124]H.H. Dam, S. Nordholm, A.Cantoni,et al. Iterative method for the design of DFTfilter bank. IEEE Trans. on Circuits and Systems II: express briefs,2004,51(11):581-586.
[125]J.Z. Jiang, P.L. Shui, Z.J. Zhang. Design of oversampled DFT-modulated filterbanks via modified Newton’s method. IET Signal Processing,2011,5(3):271-280.
[126]Z.J. Zhan. Efficient design of cosine modulated filter banks based on gradientinformation. IEEE Signal Processing Letters,2007,14(12):940-943.
[127]肖玮,涂亚庆,何丽. DTFT频谱细化特性分析及其快速算法设计.电子与信息学报,2011,33(6):1395-1400.
[128]曾小东,曾德国,张文超,等.基于OMP-SVD的多分量单频信号频率估计.雷达科学与技术,2011,9(2):188-191.
[129]P. Stoica, A. Eriksson. MUSIC estimation of real-valued sine-wave frequencies.Signal Processing,1995,42:139-146.
[130]K. Mahata, T. Soderstrom. ESPRIT-like estimation of real-valued sinusoidalfrequencies. IEEE Trans. on Signal Processing,2004,52(5):1161-1170.
[131]C.Y. Chang, S.M. Kuo. Complete direct/parallel structure for narrowband activenoise control systems. IET Signal Processing,2013,7(6):477-485.
[132]R. Roy, T. Kailath. ESPRIT–Estimation of signal parameters via rotationalinvariance techniques. IEEE Trans. Acoust, Speech Signal Processing,1989,ASSP-37(7):984-995.
[133]Y.B. Hua, X. Yong, T.P. Chen,et al. A New Look at the Power Method for FastSubspace Tracking. Digital Signal Processing,1999,9:297-314.
[134]]R. Badeau, B. David, G. Richard. Fast approximated power iteration subspacetracking. IEEE Trans. on signal processing,2005,53(8):2931-2941.
[135]P.V. Overschee, B.D. Moor. Subspace identification for linear systems: theory,implementation and applications. Kluwer Academic Publishers Dordrecht,Netherlands,1996.
[136]K. Kameyama, A. Ohsumi., Y. Matsuura, et al. Recursive4SID-basedidentification algorithm with fixed input-output data size. International Journal ofInnovative Computing, Information and Control,2005,1(3):17-33.
[137]B. Yang. Projeciton approximation subspace tracking. IEEE Trans. on SignalProcessing,1995,43(1):95-107.
[138]K.A. Meraim, A. Chkeif, Y. Hua. Fast orthonormal PAST algorithm. IEEE SignalProcessing Letters,2000,7(3):60-62.
[139]A. Valizadelh, M. Karimi. Fast subspace tracking algorithm based on theconstrained projection approximation. EURASIP Journal on Advances in SignalProcessing,2009, Article ID576972.16pages.
[140]B. Yang. An extension of the PASTd algorithm to both rank and subspace trackingIEEE Signal Processing Letters,1995,2(9):179-182.
[141]周星德,姜冬菊.结构振动主动控制.北京:科学出版社,2009,46-121.
[142]P. Li, I. Postlethwaite, M. Turner. Subsapce-based system identification forhelicopter dynamic modeling, American Helicopter Society63rd Annual Forum,Virginia Beach, VA, May1-3,2007.
[143]张贤达.矩阵分析与应用.北京:清华大学出版社,2006,89-234.
[144]李增刚. SYSNOISE Rev5.6详解.北京:国防工业出版社,2005,21-315.
[145]靳国永,杨铁军,刘志刚.弹性板封闭声腔结构-声耦合特性分析.声学学报,2007,32(2):85-96.