共查询到19条相似文献,搜索用时 156 毫秒
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分析和研究自适应滤波和小波变换法的原理及方法,提出了一种新的综合使用自适应滤波和小波变换法的语音降噪方法。该方法首先用仿生小波变换法对带噪声的语音信号进行小波分解,将小渡变换法分离出来的噪声信号作为自适应滤波器的输入。最后选择用最小均方误差(LMS)的自适应算法对带噪声语音信号进行降噪处理,实现了信噪分离,去除语音信号中的噪声信号。实验结果表明,该方法对语音信号有较为明显的降噪效果。 相似文献
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在未知系统输入信号和输出信号均含有噪声的环境中,传统的自适应滤波算法,如最小均方(LMS)算法,会产生有偏估计.总体最小二乘(TLS)算法能够同时最小化输入信号与输出信号的噪声干扰,是解决此类问题的重要方法.然而,在许多实际应用中,干扰噪声可能具有冲击特性,这使得传统基于2阶统计量的自适应滤波算法,包括总体最小二乘算法性能严重恶化,以至于不能正常工作.为了解决这个问题,该文在总体最小二乘法的基础上,利用对数函数对其改进,提出了一种能够抗冲击干扰的对数总体最小二乘(L-TLS)算法.最后,通过计算机仿真实验验证了该新算法的有效性. 相似文献
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针对稀疏多径信道下MPSK信号的快速盲均衡问题,提出了一种l0-范数约束的递归最小二乘常模盲均衡算法.该算法借鉴传统的递归最小二乘常模盲均衡算法思想,结合稀疏自适应滤波理论,首先利用l0-范数对均衡器抽头系数进行稀疏性约束,构造出一种l0-范数约束的加权最小二乘误差代价函数,然后依据递归最小二乘算法推导出均衡器抽头系数更新公式.该算法发挥递归最小二乘常模算法收敛速度快的优势,并对幅度极小系数附加零点吸引调整,从而实现不同幅度抽头系数的快速收敛.理论分析与仿真结果表明,与现有算法相比,该算法在保证较低剩余符号间干扰的前提下,能有效提高均衡器的收敛速度. 相似文献
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在未知系统输入信号和输出信号均含有噪声的环境中,传统的自适应滤波算法,如最小均方(LMS)算法,会产生有偏估计.总体最小二乘(TLS)算法能够同时最小化输入信号与输出信号的噪声干扰,是解决此类问题的重要方法.然而,在许多实际应用中,干扰噪声可能具有冲击特性,这使得传统基于2阶统计量的自适应滤波算法,包括总体最小二乘算法... 相似文献
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为了减小陀螺输出数据的时延,以满足机械抖动激光陀螺在快速跟踪中的应用,基于递归最小二乘(RLS)自适应滤波技术实现了激光陀螺抖动信号的剥除。首先对RLS自适应对消去抖算法进行了理论分析,其次通过通用串行总线接口将A/D采集的抖动反馈信号和激光陀螺计数脉冲信号传至上位机,最后基于MATLAB编写了RLS自适应程序,实现了激光陀螺抖动信号的剥除。剥除后的陀螺信号再经过11阶的有限脉冲响应滤波器和陀螺输出直接经过31阶的滤波器剩余的脉冲数基本相当,而时间延迟却明显减小。结果表明,该算法具有较快的收敛速度且能够有效去除激光陀螺计数脉冲中的抖动成分。 相似文献
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In this paper, a new spatio-temporal filtering method for removing noise from image sequences is proposed. This method combines the use of motion compensation and signal decomposition to account for the effects of object motion. Because of object motion, image sequences are temporally nonstationary, which requires the use of adaptive filters. By motion compensating the sequence prior to filtering, nonstationarities, i.e., parts of the signal that are momentarily not stationary, can be reduced significantly. However, since not all nonstationarities can be accounted for by motion, a motion-compensated signal still contains nonstationarities. An adaptive algorithm based on order statistics is described that decomposes the motion-compensated signal into a noise-free nonstationary part and a noisy stationary part. An RLS filter is then used to filter the noise from the stationary signal. Our new method is experimentally compared with various noise filtering approaches from literature. 相似文献
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An algorithm for recursively computing the total least squares (TLS) solution to the adaptive filtering problem is described. This algorithm requires O(N) multiplications per iteration to effectively track the N-dimensional eigenvector associated with the minimum eigenvalue of an augmented sample covariance matrix. It is shown that the recursive least squares (RLS) algorithm generates biased adaptive filter coefficients when the filter input vector contains additive noise. The TLS solution on the other hand, is seen to produce unbiased solutions. Examples of standard adaptive filtering applications that result in noise being added to the adaptive filter input vector are cited. Computer simulations comparing the relative performance of RLS and recursive TLS are described 相似文献
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基于级联光栅的微振动传感器是一种典型的微振动信号测量方案,然而由于光信号在级联光栅中经过多次透射和反射,导致光谱信噪比差、成分复杂等问题。基于此,文中提出一种结合经验模态分解和切比雪夫滤波技术的光谱信号优化算法。首先,将传感器原始光谱通过经验模态分解得到一系列本征模函数;其次,利用所提出的自适应滤波方法,确定包含反射峰成分的本征模函数阶数,并对其进行切比雪夫低通滤波;最后,将滤波器输出进行重构,即得到优化后的传感器光谱。使用振幅为±8 mV、频率为500 Hz的微振动激励信号进行实验验证。结果表明:文中所提出算法可以较好地还原激励源发出的微振动信号,相比传统方法精度提高87.5%以上。 相似文献
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中值滤波系列算法在处理被不同密度椒盐噪声污染的细节图像和平坦图像时,降噪性能不一致。本文借鉴开关中值滤波和压缩感知的思想,提出了随机采样滤波算法去除椒盐噪声。算法以噪声检测为基础,将被椒盐噪声污染的图像分为疑似噪声像素和信号像素,随机采样仅对信号像素采样。然后,利用正交匹配追踪算法重构出被污染前的图像,替代了中值滤波对噪声像素的估计。由于随机采样滤波基于压缩感知理论,对稀疏信号的重构具有最少测量次数的条件,因此随机采样点的数量具有一定的浮动空间,表现为对噪声密度不敏感。以被不同噪声密度污染图像的纹理、平坦局部区域进行验证,实验表明,当噪声密度在一定范围内变化时,算法可以实现对噪声密度不敏感。在高密度噪声污染的情况下,相较于中值滤波系列算法,随机采样滤波算法具有更好的细节保留能力和滤波能力。对标准测试图像进行了全局滤波,不同噪声密度具有一致的滤波效果,与自适应滤波算法相比,随机采样滤波算法在处理包含密集边缘特征的区域时更具备优势。 相似文献
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本文为了在语音信号处理中能消除含噪语音信号中的背景噪音,采用自适应信号处理的理论和技术来达到提高语音信号质量的目的。通过介绍自适应滤波器原理,在对自适应滤波器相关理论研究的基础上,研究了LMS自适应滤波算法,并对LMS自适应算法进行了分析。同时为了使输入的参考信号与噪声相关,加入分离周期信号与带有窄带干扰抑制的宽带信号。通过分析仿真结果表明基于LMS算法的自适应噪声抵消技术可以有效地抵消正弦干扰信号,同时加入宽带信号中的周期性噪声,在没有另外的与噪声相关的参考信号的情况下,可以使用自适应噪声抵消系统来消除这种同期性干扰噪声。 相似文献
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Zou Y. Chan S.C. Ng T.S. 《Vision, Image and Signal Processing, IEE Proceedings -》2001,148(4):289-294
An M-estimate adaptive filter for robust adaptive filtering in impulse noise is proposed. Instead of using the conventional least-square cost function, a new cost function based on an M-estimator is used to suppress the effect of impulse noise on the filter weights. The resulting optimal weight vector is governed by an M-estimate normal equation. A recursive least M-estimate (RLM) adaptive algorithm and a robust threshold estimation method are derived for solving this equation. The mean convergence performance of the proposed algorithm is also analysed using the modified Huber (1981) function (a simple but good approximation to the Hampel's three-parts-redescending M-estimate function) and the contaminated Gaussian noise model. Simulation results show that the proposed RLM algorithm has better performance than other recursive least squares (RLS) like algorithms under either a contaminated Gaussian or alpha-stable noise environment. The initial convergence, steady-state error, robustness to system change and computational complexity are also found to be comparable to the conventional RLS algorithm under Gaussian noise alone 相似文献
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《IEEE transactions on bio-medical engineering》1996,43(5):480-492
Presents the time-warped polynomial filter (TWPF), a new interval-adaptive filter for removing stationary noise from nonstationary biomedical signals. The filter fits warped polynomials to large segments of such signals. This can be interpreted as low-pass filtering with a time-varying cutoff frequency. In optimal operation, the filter's cut-off frequency equals the local signal bandwidth. However, the author also presents an iterative filter adaptation algorithm, which does not rely on the (complicated) computation of the local bandwidth. The TWPF has some important advantages over existing adaptive noise removal techniques: it reacts immediately to changes in the signal's properties, independently of the desired noise reduction; it does not require a reference signal and can be applied to nonperiodical signals. In case of quasiperiodical signals, applying the TWPF to the individual signal periods leads to an optimal noise reduction. However, the TWPF can also be applied to intervals of fixed size, at the expense of a slightly lower noise reduction. This is the way nonquasiperiodical signals are filtered. The author presents experimental results which demonstrate the usefulness of the interval-adaptive filter in several biomedical applications: noise removal from ECG, respiratory and blood pressure signals, and base-line restoration of electroencephalograms (EEGs) 相似文献
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采用多扫描自适应预测的红外弱目标检测 总被引:1,自引:0,他引:1
提出了一种多扫描预平滑RLS自适应算法用于增强红外成像数据中的弱目标检测.感兴趣的目标被假设为只有极小的空域扩展度,而且淹没于强背景杂波干扰中.通过RLS自适应滤波器,背景杂波分量被准确地预测并从输入信号中去除,从而只剩下目标信号与残留噪声.在全空域非平稳数据中应用多扫描机制可以增强算法对非平稳杂波的跟踪性能;而将原始图像数据经过预平滑处理后作为自适应滤波器的输入,则能够减少由于目标灰度扩展带来的背景预测失准.对真实图像数据的仿真表明该算法的性能明显优于其它几种传统方法. 相似文献