自适应滤波RLS算法研究与DSP实现

自适应滤波器一直是随机信号处理当中的一个重要的研究课题,可以不需要预先知道给定信号及噪声的自相关函数,它可以利用前一时刻已获得的滤波器参数自动地调节现时刻的滤波器参数,从而实现最佳滤波.研究了自适应滤波器的基本结构和自适应滤波递推最小二乘(RLS)算法在DSP上的实现,采用定点TMS320VC5416 DSP为平台,通过C语言设计了一个RLS算法的16阶自适应滤波器,在CCS环境下进行仿真结果查看,并通过与MATLAB进行仿真比较.实验结果表明,该滤波器有效、性能良好,实现了较好的滤波功能,在数字信号处理领域有很好的应用价值.     

基子自适应卡尔曼滤波器的MEMS陀螺零点漂移的研究

随机噪声是影响MEMS陀螺精度的一个重要因素.本文基于时间序列分析方法建立MEMS陀螺的随机漂移AR模型后,使用自适应卡尔曼滤波器对信号进行滤波.通过比较陀螺原始信号和自适应卡尔曼滤波后的信号,可以得出结论:自适应卡尔曼滤波器在处理MEMS陀螺零点漂移中具有良好的滤波效果.     

手术机器臂前端控制器双重滤波的噪声抑制

医生在机械臂的辅助下手术时常会因为末端抖动而导致手术不稳定.小波滤波和自适应滤波相结合的方式对前端控制器信号进行滤波,由于自适应滤波器具有可以实时滤波的特性,采用自适应滤波器对含有不同方差的高斯白噪声的正弦信号模拟的信号进行滤波,均方根误差和误差率作为评价标准.实验结果表明,使用小波滤波预滤波后的信号通过自适应滤波可以...     

基于TMS320C6713DSK的自适应滤波器实现

自适应滤渡器可时信号实现最优的滤波效果.简要介绍了利用TI公司的TMS320C 6713DSK实现自适应滤波器的设计方案,自适应滤波算法采用易于实现的LMS算法.通过对一段音频信号的仿真实验证明:利用DSP实现的自适应滤波能够实时地对信号进行处理,在运算速度上要比其他的软件实现的自适应滤波器快很多.     

基于自适应卡尔曼滤波器的MEMS陀螺零点漂移的研究

随机噪声是影响MEMS陀螺精度的一个重要因素。本文基于时间序列分析方法建立MEMS陀螺的随机漂移AR模型后,使用自适应卡尔曼滤波器对信号进行滤波。通过比较陀螺原始信号和自适应卡尔曼滤波后的信号,可以得出结论：自适应卡尔曼滤波器在处理MEMS陀螺零点漂移中具有良好的滤波效果。     

基于DSP的自适应滤波器的实现

在数字信号处理过程中,常常要处理一些无法预知的信号、噪声或时变信号,如果采用具有固定滤波系数的数字滤波器无法实现最优滤波。实现较好的滤波,必须设计自适应滤波器,以跟踪信号和噪声的变化。主要根据自适应滤波的结构及原理并对最小均方误差(LMS)算法进行了研究,同时在DSP的集成开发环境下利用C语言编程设计一个16阶LMS自适应滤波器,并在软件模拟器上实现了仿真。通过不断调整滤波器的自适应步长,对实验结果进行对比分析,最后给出了结论。     

基于自适应仿生小波变换的语音增强方法

分析自适应滤波和小波滤波的原理与方法,提出一种基于自适应仿生小波变换的语音增强方法.该方法首先用仿生小波变换对含噪语音信号进行小波分解,这样可以保证对信号频率和幅值的听觉特性,然后将经仿生小波变换所分离出来的噪声成分作为自适应滤波器的输入.通过选用自适应滤波器的最小二乘算法(RLS)从而实现信噪分离的最佳滤波,以保证去除信号中的相关噪声.实验结果表明,该方法对语音信号有显著的增强效果,能实现语音信号在同频段对噪声成分和有用信号的最佳估计.     

自适应滤波器的收敛性研究

主要介绍基于最小均方算法的自适应滤波器。该自适应滤波器的显著特点是滤波频率可以自适应地跟踪输入信号的频率变化，而且它的品质因数Q和滤波频率可以独立编程，Matlab仿真结果表明该算法具有良好的收敛性。给出了自适应滤波器的设计方法以及参数：步长、滤波器长度和初始系数向量对滤波器性能的影响，分析了算法的收敛性能和稳定特性。     

基于纹理分析的改进型Nagao滤波器

介绍了Nagao滤波器的基本原理,针对传统Nagao滤波器滤波后图像存在伪像以及用计算机实现处理速度慢等缺点,提出了一种基于纹理分析的改进型Nagao滤波器.通过对图像进行纹理分析,得到图像纹理复杂度指标,并用该指标作为Nagao滤波器选择模板的参数.实验分析表明,该方法可以很好地克服传统Nagao滤波器的不足,在进行图像自适应滤波时取得了比较好的效果,非常适合于对计算机处理速度要求比较高的应用.     

基于MATLAB与DSP的自适应滤波器设计与实现

滤波是滤除信号中某些特定频率的波形的技术,在数字信号处理中,主要用于滤除噪声和干扰信号。由于噪声和干扰信号的不确定性,采用固定滤波系数的数字滤波器无法达到最佳的效果。自适应滤波器能够随着环境的改变而改动滤波器自身的参数和结构,从而能够随着噪声和干扰信号的不断变化修正滤波器的参数和结构,最终实现较理想的滤波。本文研究了最小均方差(LMS)算法,并结合自适应滤波器的结构和原理,设计出FIR结构自适应滤波器。最后给出MATLAB仿真结果,并利用DSP验证自适应滤波器的性能。     

Adaptive filters with constraints and correlated non-stationary signals

We consider the algorithm for adaptive filters with contraints or adaptive filters of beam former type. The signals are non-stationary and correlated. Under rather weak conditions, we obtain the strong consistency result.     

自动检测图像目标的形态滤波遗传算法

提出了一种实现形态滤波器参数优化设计的贵传学习算法（Genetic Training Algorithm for Morphologi-cal Fitters,GTAMF）。采用新的交叉与变异算子－曲面体交叉与主从式变异,通过优化搜索全局以获得滤波性和时效性兼优的形态滤波器参数。实验结果表明该方法设计方便,实用性强且易于推广,对提高形态滤波性能效果明显,分析表明,形态滤波器可分解为形态学运算和结构元选择两个基本问题,形态学运算的规则已由定义本身而确定,于是形态滤波器的最终滤波性能就仅仅取决于结构元的选择。通过自适应优化训练使结构元具有图像目标的形态结构特征,从而赋予结构元特定的知识,使形态滤波过程融入特有的智能,以实现对复杂变化的图像具有良好的滤波性能和稳健的适应能力。     

Root Images of Median Filters

Median filters are frequently used in signal analysis because of their smoothing properties and their insensitivity with respect to outliers in the data. Since median filters are nonlinear filters, the tools of linear theory are not applicable to them. One approach to deal with nonlinear filters consists in investigating their root images (fixed elements or signals transparent to the filter). Whereas for one-dimensional median filters the set of all root signals can be completely characterized, this is not true for higher dimensional filters.In 1989, Döhler stated a result on certain root images for two-dimensional median filters. Although Döhlers results are true for a wide class of median filters, his arguments were not correct and his assertions do not hold universally. In this paper we give a rigorous proof of Döhlers results. Moreover, his approach is generalized to the d-dimensional case.     

一类自适应顺序形态滤波器

提出了一类自适应顺序形态滤波器,建立了排序运算的隐含表达式,并在最小均方误差（ＭＳＥ）和最小平均绝对误差（ＭＡＥ）准则下,实现了结构元素和百分位值的自适应处理,该滤波器不仅可以有效地抑制信号中的各种噪声,而且较好地保持了信号的几何特征,计算机仿真结果证实了滤波算法在噪声图象恢复方面有较好的效果。     

An adaptive coherence filter

This paper derives an adaptive coherence filter for canceling interference from a signal of interest whose power spectral density is symmetric. A basic property of the Fourier transform of real signals is that their spectra are Hermitian symmetric. This property is exploited to determine which part of a spectrum is interference and which part is the signal of interest. An unconstrained Wiener filter is derived that exploits the frequency domain symmetry of the signal of interest. While the adaptive coherence filter is based on the Fourier transform property of real signals, an extension of the algorithm is provided so the filter can be used on any signal that displays spectral symmetry. A practical method for implementing the filter is provided. The filter has application in the area of telecommunications, but is applicable in wireless communication applications where a signal, that displays spectral symmetry, is corrupted by interfering signals within the signal of interest's bandwidth.     

On-line identification scheme using adaptive filters

An on-line scheme for identifying a linear process is proposed which consists of a linear time-varying filter and a parameter update algorithm. The disturbances affecting the process, its input and its output, belong to a general class of signals which are a mixture of stochastic and deterministic signal processes generated by some linear time-invariant system excited by white noise and the Dirac delta function, respectively. The process and the disturbance signal models are not restricted to be asymptotically stable. Either a probing input signal or a normal operating input signal can be employed. The probing signal consists of a finite number of sinusoidal signals (exponentially increasing sinusoidal signals for unstable processes) of distinct frequencies. When a normal operating signal is used, an adaptive scheme is employed to tune the parameters of the filters to the distinct frequency components of the signal. The convergence of the parameter estimates to their true value is established.     

基于线性T-S模糊系统的自适应系统的设计和稳定性分析

提出了一种新型的模糊自适应控制器.其中系统的未知函数由线性T-S模糊系统估计,模糊规则的后件是线性可调的.因线性T-S模糊系统较之Mamdani模糊系统具有更好的估计性能,我们可以获得更小的跟踪误差.由于采用李亚普诺夫合成方法来设计控制器,整个闭环系统全局稳定,所有的信号有界.对倒立摆的仿真验证了这一点.     

Acoustic signal separation of mechanical devices using ICA with band-pass filters

We propose a method of separating the acoustic signals of motors and the gears of mechanical devices by using independent component analysis (ICA) with band-pass filters. The frequency distribution of a recorded acoustic signal from the operating mechanical device can be divided into three fields, the low-frequency field, which corresponds to the frequency characteristics of the gear, the medium-frequency field, which is mixed with the frequency characteristics of the gear and the motor, and the high-frequency field, which corresponds to the frequency characteristics of the motor. Since only the medium-frequency components are a mixture of the acoustic signals of gears and motors, ICA with band-pass filters is expected to separate the acoustic signals of motors and gears more accurately than conventional ICA. The simulation and experimental results show that the proposed method can separate the acoustic signals of motors and gears of mechanical devices successfully.This work was presented, in part, at the 9th International Symposium on Artificial Life and Robotics, Oita, Japan, January 28–30, 2004     

Adaptive noise filtering based on artificial hydrocarbon networks: An application to audio signals

Many audio signal applications are corrupted by noise. In particular, adaptive filters are frequently applied to white noise reduction in audio. Recent work provides that there exist some insights on using an artificial intelligence method called artificial hydrocarbon networks (AHNs) for filtering audio signals. Thus, the scope of this paper is to design and implement a novel approach of artificial hydrocarbon networks on adaptive filtering for audio signals. Three experiments were developed. Results demonstrate that AHNs can reduce noise from audio signals. A comparison between the proposed algorithm and a FIR-filter is also provided. The short-time objective intelligibility value (STOI) and the signal-to-noise ratio (SNR) were used for evaluation. At last, the proposed training method for finding the parameters involved in the AHN-filter can also be used in other fields of application.     

基于粒子群优化算法的自适应IIR滤波器设计

针对自适应无限冲激响应(infinite impulse response,IIR)数字滤波器的设计实质上是一个多参数优化问题,提出了一种用粒子群优化算法(particle swarm optimization,PSO)设计IIR数字滤波器的方法.将滤波器的设计问题转化为滤波器参数的优化问题,利用粒子群优化算法对整个参数空间进行高效并行搜索以获得参数的最优化,基于多个典型系统的随机数值仿真以及与最小二乘方法的比较研究,验证了该方法的有效性、全局性和对初值的鲁棒性.