有源噪声控制的一种变换域算法

有源噪声控制广泛应用于中低频噪声控制中。传统的有源控制都是在时域内使用LMS算法进行控制，由于LMS算法的收敛速度跟输入信号的自相关矩阵特征值分布有关，当特征值分布较大时，LMS算法的收敛速度会变的很慢。本文使用离散余弦变换(DCT)技术，在变换域内进行有源噪声控制的研究，同时考虑了变换域约束。仿真表明算法的有效性。     

汽车车内噪声主动控制系统仿真

摘要：降低汽车空腔的振动,是抑制汽车车内噪声的有效途径之一。以激振器、作动器和控制器等为主要部件,搭建了简化的汽车车内噪声主动控制系统,该系统通过将汽车空腔模型简化为板件,以减弱板件振动为目标,实现了汽车车内噪声主动控制。采用简谐正弦及余弦信号作为激振器发出的激励,用于模拟板件的初始振动,控制器通过采用模糊控制算法直接控制压电陶瓷作动器的振动,压电陶瓷作动器的振动用于抑制板件的振动,完成了汽车车内噪声主动控制系统仿真。仿真结果表明,研究采用的汽车车内噪声主动控制系统,使汽车空腔振动降低23%,为解决由汽车发动机和动力总成的振动所引发的汽车车内噪声问题提供了一个有效途径。     

空间有源消声实验研究

本文分别在半声室和普通房间进行有源消声实验，证明了作者以前提出的算法是收敛的，稳定的，以此算法构成的多通道有源消声系统对直达声和混响声都能进行了有效的控制。     

基于变结构技术的噪声有源自校正控制

研究有源噪声控制（ＡＮＣ）问题，给出了有源噪声控制问题的一般模型。研究表明，采用前馈和反馈技术的有源噪声控制分别与控制理论中的干扰可测和干扰不可测的自适应控制相对应。提出了一种基于滑模变结构技术的噪声有源自适应控制方法，可以处理所有模型参数均未知的有源噪声控制问题，给出了一种未知模型参数的在线自适应学习算法。证明了闭环控制系统在Ｌｙａｐｕｎｏｖ意义下的稳定性。仿真结果表明，基于滑模变结构技术的噪声有源自适应控制是一种非常有效的控制方法。     

多通道窄带Fx-Newton时频算法及动力机械有源隔振实验

提出了一种多通道窄带Fx-Newton算法,用多个控制器并联控制各窄带带通滤波器提取的多根线谱。离线辨识次级通道频响矩阵并求逆后,只需L×L个(通道数为L)2阶滤波器即可实现次级通道逆模型滤波,从而使控制器系数向最优值迅速收敛。还提出了窄带滤波相位差的自适应补偿算法,可提高线谱频率波动时的控制鲁棒性。在200 kW实船柴油发电机有源无源隔振装置上进行了实验,结果表明该算法可同时控制10~11根线谱,使每根线谱衰减10~35 dB。该算法运算量小,在保证收敛稳定性的同时、收敛速度大于FxLMS算法(尤其在次级通道矩阵特征值分散度大的情况)。     

多通道窄带Fx-Newton时频算法及动力机械有源隔振实验

提出了一种多通道窄带Fx—Newton算法,用多个控制器并联控制各窄带带通滤波器提取的多根线谱。离线辨识次级通道频响矩阵并求逆后,只需L×L个(通道数为L)2阶滤波器即可实现次级通道逆模型滤波,从而使控制器系数向最优值迅速收敛。还提出了窄带滤波相位差的自适应补偿算法,可提高线谱频率波动时的控制鲁棒性。在200kw实船柴油发电机有源无源隔振装置上进行了实验,结果表明该算法可同时控制10~11根线谱,使每根线谱衰减10~35dB。该算法运算量小,在保证收敛稳定性的同时、收敛速度大于FxLMS算法(尤其在次级通道矩阵特征值分散度大的情况)。      

基于无限脉冲响应滤波器的自适应滤波E型有源噪声控制算法

相对于有限脉冲响应(FIR)滤波器,无限脉冲响应(ⅡR)滤波器由于其本身自有的零-极点结构,能以较低的阶数即可与系统模型匹配。因此在有源噪声控制(ANC)中,使用ⅡR滤波器可以大大节省计算量并能提高系统效率。自适应滤波U型最小均方差(FULMS)算法是目前常用的基于ⅡR滤波器的有源噪声控制算法,但该算法却不能保证全局收敛,这大大限制了该算法在有源噪声控制中的应用。本文提出一种新的基于ⅡR滤波器的自适应有源噪声控制算法——滤波E型最小均方差算法,该算法计算量相对于FULMS算法仅略有增加,但具有良好的全局收敛性,仿真结果表明该算法具有良好的降噪效果。     

一种能量比调控的有源噪声控制算法*

在前馈有源噪声控制系统中,建模信号与控制信号相互影响,建模信号的引入会导致系统降噪性能变差。为了减小建模信号的影响,提出一种基于能量比调控的次级通道在线建模有源噪声控制算法。利用控制过程与建模过程的误差能量比构造步长调控函数,分别调节控制过程与建模过程的步长值,从而减弱两者的相互影响。在次级通道建模过程中,对建模步长值采取分段调控的方法,并通过建模步长值的变化来调节建模信号,从而提升系统降噪性能。仿真结果表明,对于低频噪声信号的有源噪声控制,相比已有算法,提出的算法能获得较快的建模收敛速度和较高的降噪量。     

基于时间分辨测量的近红外光三维层析成像研究

针对近红外漫射光层析成像中的三维成像问题展开了理论和实验研究.利用开发的以强度和平均飞行时间作为数据类型的三维重建算法对数值仿真模型进行了重建,同时采用多通道时间相关单光子计数层析成像系统对固体样品进行了三维成像.结果表明：所发展的算法较忠实地重建出了样品内部的异质体结构;采用的差分重建方法有利于提高重建的抗噪性能;提出的单层测头配置模式对于三维成像具有实际意义.     

无次级通道建模有源噪声控制算法的实验研究

以管道中有源噪声控制为实验平台,基于ADSP21161N数字信号处理评估板实现了本文作者早先提出的不需要次级通道建模的基于LMS方法的四方向搜索型有源控制算法,并给出了该算法在实际应用中收敛系数的选取原则。针对单频和多频噪声的实验结果表明,前人两方向搜索型有源控制算法在大部分频率上都能收敛,但在次级通道相位为±90°的频率不能收敛,而本文作者所提出的四方向变收敛系数搜索型算法,在所有频率都能快速收敛。     

A novel feedback active noise control for broadband chaotic noise and random noise

The feedback active noise control (ANC) can be seen as a predictor, the conventional method based on filtered-x least mean square (FXLMS) algorithm can only be useful for linear and tonal noise, but for nonlinear and broadband noise, it is useless. The feedback ANC using functional link artificial neural networks (FLANN) based on filtered-s least mean square (FSLMS) algorithm can reduce some nonlinear noise such as chaotic noise, but the noise cancellation performance is not very well, at the same time, it is not useful to random noise. To solve the problem above, a new feedback ANC using wavelet packet FXLMS (WPFXLMS) algorithm is proposed in this paper. By decomposing the broadband noise into several band-limited parts which are predictable and each part is controlled independently, the proposed algorithm can not only suppress the chaotic noise, but also mitigate the random noise. Compared with FXLMS and FSLMS algorithms, proposed WPFXLMS algorithm also holds the best performance on noise cancellation. Numerous simulations are conducted to demonstrate the effectiveness of the proposed WPFXLMS algorithm.     

Improving performance of FxRLS algorithm for active noise control of impulsive noise

Active noise control (ANC) systems employing adaptive filters suffer from stability issues in the presence of impulsive noise. New impulsive noise control algorithms based on filtered-x recursive least square (FxRLS) algorithm are presented. The FxRLS algorithm gives better convergence than the filtered-x least mean square (FxLMS) algorithm and its variants but lacks robustness in the presence of high impulsive noise. In order to improve the robustness of FxRLS algorithm for ANC of impulsive noise, two modifications are suggested. First proposed modification clips the reference and error signals while, the second modification incorporates energy of the error signal in the gain of FxRLS (MGFxRLS) algorithm. The results demonstrate improved stability and robustness of proposed modifications in the FxRLS algorithm. However, another limitation associated with the FxRLS algorithm is its computationally complex nature. In order to reduce the computational load, a hybrid algorithm based on proposed MGFxRLS and normalized step size FxLMS (NSS-FXLMS) is also developed in this paper. The proposed hybrid algorithm combines the stability of NSS-FxLMS algorithm with the fast convergence speed of the proposed MGFxRLS algorithm. The results of the proposed hybrid algorithm prove that its convergence speed is faster than that of NSS-FxLMS algorithm with computational complexity lesser than that of FxRLS algorithm.     

RETRACTED: A Hybrid Nonlinear Active Noise Control Method Using Chebyshev Nonlinear Filter

Investigations into active noise control (ANC) technique have been conducted with the aim of effective control of the low-frequency noise. In practice, however, the performance of currently available ANC systems degrades due to the effects of nonlinearity in the primary and secondary paths, primary noise and louder speaker. This paper proposes a hybrid control structure of nonlinear ANC system to control the non-stationary noise produced by the rotating machinery on the nonlinear primary path. A fast version of ensemble empirical mode decomposition is used to decompose the non-stationary primary noise into intrinsic mode functions, which are expanded using the second-order Chebyshev nonlinear filter and then individually controlled. The convergence of the nonlinear ANC system is also discussed. Simulation results demonstrate that proposed method outperforms the FSLMS and VFXLMS algorithms with respect to noise reduction and convergence rate.     

A nonlinear active noise control algorithm for virtual microphones controlling chaotic noise

In active noise control (ANC) systems, virtual microphones provide a means of projecting the zone of quiet away from the physical microphone to a remote location. To date, linear ANC algorithms, such as the filtered-x least mean square (FXLMS) algorithm, have been used with virtual sensing techniques. In this paper, a nonlinear ANC algorithm is developed for a virtual microphone by integrating the remote microphone technique with the filtered-s least mean square (FSLMS) algorithm. The proposed algorithm is evaluated experimentally in the cancellation of chaotic noise in a one-dimensional duct. The secondary paths evaluated experimentally exhibit non-minimum phase response and hence poor performance is obtained with the conventional FXLMS algorithm compared to the proposed FSLMS based algorithm. This is because the latter is capable of predicting the chaotic signal found in many physical processes responsible for noise. In addition, the proposed algorithm is shown to outperform the FXLMS based remote microphone technique under the causality constraint (when the propagation delay of the secondary path is greater than the primary path). A number of experimental results are presented in this paper to compare the performance of the FSLMS algorithm based virtual ANC algorithm with the FXLMS based virtual ANC algorithm.     

A robust filtered-s LMS algorithm for nonlinear active noise control

The performance of a nonlinear active noise control (ANC) system based on the recently developed filtered-s least mean square (FsLMS) algorithm deteriorates when strong disturbances in the ANC system are acquired by the microphones. To surmount this shortcoming, a novel robust FsLMS (RFsLMS) algorithm is proposed for a functional link artificial neural network (FLANN) based ANC system. The new ANC system is least sensitive to such disturbances and does not call for any prior information on the noise characteristics. The results obtained from simulation study establish the effectiveness of this new ANC scheme.     

一种用于主动降噪耳机的权重滤波误差信号滤波-x最小均方算法*

针对传统FxLMS算法前馈自适应主动降噪耳机系统因果性条件不足时在宽带噪声环境中产生的高频噪声抬升问题,该文引入权重滤波误差信号FxLMS算法用于抑制高频噪声的抬升,但该算法带来了低频降噪量不足问题。因此,进一步提出将固定系数混合控制器与权重滤波误差信号FxLMS算法结合,在解决高频噪声抬升问题的同时,保证了良好的低频降噪量。基于DSP平台实现了提出的主动降噪耳机方案。实验证明,该方案针对宽带和单频等噪声都取得了较好的降噪效果。     

参量阵扬声器在管道噪声控制中的研究*

为了解决管道有源噪声控制中声反馈造成的系统复杂度和计算量的增加,文中引入参量阵扬声器作为次级声源,利用其强指向性减小控制系统的声反馈。为了验证该方法可行性,本文分别在直管和L管中,对600 Hz单频噪声和频率范围为500 Hz~1000 Hz的窄带噪声进行了管道有源噪声控制,同时测量了参量阵扬声器的管内声场和降噪范围。结果表明,参量阵扬声器声反馈小,在没有声反馈补偿的条件下对单频噪声的降噪效果基本达到了声反馈补偿条件下普通扬声器的降噪效果,对窄带噪声的降噪效果稍差。此外,通过测量管道声场和降噪量,确定了参量阵扬声器的降噪区域为误差传感器下游整个管道,降噪面积为管道整个截面。这说明参量阵扬声器作为次级声源降低了系统的复杂度和算法的计算量,并取得了较好的降噪效果。     

Improving robustness of filtered-x least mean p-power algorithm for active attenuation of standard symmetric-α-stable impulsive noise

The paper concerns active control of impulsive noise having peaky distribution with heavy tail. Such impulsive noise can be modeled using non-Gaussian stable process for which second order moments do not exist. The most famous filtered-x least mean square (FxLMS) algorithm for active noise control (ANC) systems is based on the minimization of variance (second order moment) of error signal, and hence, becomes unstable for the impulsive noise. In order to improve the robustness of adaptive algorithms for processes having distributions with heavy tails (i.e. signals with outliers), either (1) a robust optimization criterion may be used to derive the adaptive algorithm or (2) the large amplitude samples may be ignored or replaced by an appropriate threshold value. Among the existing algorithms for ANC of impulsive noise, one is based on the minimizing least mean p-power (LMP) of the error signal, resulting in FxLMP algorithm (approach 1). The other is based on modifying; on the basis of statistical properties; the reference signal in the update equation of the FxLMS algorithm (approach 2). In this paper we propose two solutions to improve the robustness of the FxLMP algorithm. In first proposed algorithm, the reference and the error signals are thresholded before being used in the update equation of FxLMP algorithm. As another solution to improve the performance of FxLMP algorithm, a modified normalized step size is proposed. The computer simulations are carried out, which demonstrate the effectiveness of the proposed algorithms.