改进子带自适应滤波算法及其在回波抵消中的应用

本文提出了两种基于多带结构的仿射投影符号子带自适应滤波器(Affine Projection Sign Subband Adaptive Filter, AP SSAF)的改进方法。针对稀疏系统的系统识别,设计了两种子带自适应滤波器。首先给出了AP SSAF的变正则化参数更新方程,文中采用随机梯度下降法来更新正则化参数,来使系统的均方偏差最小化,该方法能同时兼顾快速收敛及低稳态失调。其次将权重分布矩阵引入AP SSAF得到系数比例AP SSAF,该方法能够利用系统的稀疏性提高AP SSAF的收敛性能。仿真中将本文所提算法用于一般系统识别以及回波抵消,实验结果验证了本文的算法对脉冲噪声具有稳健性,具有较好的跟踪性能,并具有较快的收敛速度及低稳态失调。      

一种成比例系数的子带自适应啸叫抑制算法

啸叫现象会严重影响扩声系统性能.采用自适应滤波算法辨识反馈路径的方式进行啸叫抑制,将成比例系数的无延时多带结构子带自适应滤波(Proportionate Delayless Multiband-structured Subband Adaptive Filtering,PDM-SAF)算法应用到啸叫抑制系统中.该算法继承了子带自适应滤波算法收敛速度快的优点,并考虑到反馈路径的稀疏特性和系统对实时性的要求,采用成比例系数的步长控制和无延时的误差计算方法.仿真结果表明,当扩声系统的反馈路径具有稀疏特性时,PDMSAF算法可以加快自适应滤波的收敛和跟踪速度.     

一种变正则化矩阵的改进多带结构子带自适应滤波算法

定正则化因子的改进多带结构子带自适应滤波(IMSAF)算法在取得收敛速度快和稳态失调误差小之间存在冲突.根据系统噪声抵消原理,设定子带后验误差功率等于子带噪声功率,本文提出了变正则化矩阵的IMSAF算法来解决这一问题.仿真结果证明,所提算法可以同时达到收敛速度快、稳态失调误差小以及追踪速度快等优势.     

基于SHAKF滤波器的MEMS陀螺随机误差建模补偿

MEMS陀螺随机漂移误差是制约惯性导航精度的关键因素。本文针对标准kalman滤波器陀螺漂移处理方法中,随机动态系统的结构参数和噪声统计特性参数不准确的问题,采用自适应SHAKF(Sage-Husa Adaptive Kalman Filter)滤波器进行参数实时估计,提高陀螺漂移精度。基于此思想,建立了ARMA随机误差模型,搭建了MEMS陀螺组件实验系统,通过高精度三轴转台静态测试采集陀螺数据。Aallan方差分析表明,零偏不稳定性经线性KF滤波后提升17.4%,经自适应SHAKF滤波后提升26.2%。     

9.6kb/s时域谐波压扩与自适应比特分配子带编码系统

本文提出了一种新型的9.6kb/s时域谐波压扩(Time Domain Harmonic Scaling,简称TDHS)与自适应比特分配(Adaptive Bit Allocation,简称AB)子带编码(Subband Coding,简称SBC)系统(TDHS+SBC—AB),该系统采用了三子带等带宽子带编码。文中提出了一种根据各子带余量信号的最大幅度进行自适应比特分配的算法,并提出了一种新的比特调整方法,减少了运算量,简化了系统。本文还提出了以不同的帧长分别进行TDHS处理和SBC编码的新编码结构,有效地改善了重建语音的质量。并对系统的比特分配进行了优化。对典型汉语测试语句的计算机模拟结果表明,本文所设计的系统的重建语音主观质量优于已有的16kb/s SBC—AB系统,重建语音的自然度和清晰度都较高。     

稳健的基于参数化协方差矩阵估计的空时自适应处理方法

参数化协方差矩阵估计（Parametric Covariance Matrix Estimation,PCE）方法利用雷达系统参数估计杂波协方差矩阵（Clutter Covariance Matrix,CCM）,显著提升非均匀环境下空时自适应处理（Space-Time Adaptive Processing,STAP）的性能;但是在系统参数和杂波分布存在误差情况下,性能下降严重.本文提出一种稳健的基于PCE方法的STAP杂波抑制方法.首先利用稀疏恢复方法与Radon变换估计杂波分布,然后提出一种归一化广义内积统计量修正杂波的分布,最后利用PCE方法估计CCM并进行STAP杂波抑制.通过分析舰载高频地波雷达仿真和实测数据处理结果表明：所提方法的稳健性大幅提升,相比稀疏恢复STAP方法和前后向空时平滑STAP方法滤波器凹口更加准确且更深,在有效抑制杂波的同时更利于慢速目标的检测.     

高斯和高阶无迹卡尔曼滤波算法

为了提高非线性变换的近似精度,提出了一种高阶无迹变换（High order Unscented Transform,HUT）机制,利用HUT确定采样点并进行数值积分去近似状态的后验概率密度函数,建立了高阶无迹卡尔曼滤波（High-order Unscented Kalman Filter,HUKF）算法.进一步的为了解决非线性、非高斯系统的状态估计问题,将HUKF与高斯和滤波（Gaussian Sum Filter,GSF）相结合,提出了一种高斯和高阶无迹卡尔曼滤波算法（Gaussian Sum High order Unscented Kalman filter,GS-HUKF）,该算法的核心思想是利用一组高斯分布的和去近似状态的后验概率密度,同时针对每一个高斯分布采用高阶无迹卡尔曼滤波算法进行估计.数值仿真实验结果表明,提出的HUT机制与普通的无迹变换（Unscented Transform,UT）相比,具有更高的近似精度;提出的GS-HUKF与传统的GSF以及高斯和粒子滤波器（Gaussian Sum Particle Filter,GS-PF）相比,兼容了二者的优点,即具有计算复杂度低和估计精度高的特性.     

基于自适应Kalman滤波的二维有噪子带信号恢复

基于子带信号的多通道表示（multichannel representation)和输入信号的动态特征,本文尝试推出了一种多分辨率状态空间模型,它与带相加子带噪声的滤波器组（Filter Bank)系统是等价的,于是使有噪子带信号的恢复可表述为相应多分辨率态空间模型的最优状态估计问题。进一步又利用信号的向量动态模型,发展了适于二维Kalman滤波的二维多分辨率状态空间模型,根据信号行为的分布,目标平面（object plane)可分割为不同的区域并用不同的向量动态模型来表征信号的非平衡分布,计算机数字仿真结果进一步证实了本文所提出了二维多分辨率Kalman滤波器性能的优越性。     

分布式子带自适应滤波算法

为了解决分布式最小均方算法在输入信号相关性较高时收敛速度较慢、分布式仿射投影算法计算复杂度较高等问题,本文提出了两种分布式子带自适应滤波算法,即递增式和扩散式子带自适应滤波算法.分布式子带自适应滤波算法将节点信号进行子带分割来降低信号的相关性,从而加快收敛速度.由于用于子带分割的滤波器组中包含了抽取单元,所以分布式子带自适应滤波算法和对应的分布式最小均方算法的计算复杂度相近.仿真结果表明,与分布式最小均方算法相比,分布式子带自适应滤波算法具有更好的收敛性能.     

基于变步长SOCP的宽带发射鲁棒唯相位ADBF算法

针对宽带自适应发射数字波束的权矢量非恒模、高动态环境下干扰抑制能力下降的问题，提出了一种基于变步长迭代二阶锥(Second-Order Cone Programming, SOCP)的宽带发射鲁棒唯相位自适应波束形成(Adaptive Digital Beam Forming, ADBF)算法。采用抽头延迟线结构(Tapped Delay Line, TDL)并结合多相滤波器(Polyphase Filter, PF)对宽带信号进行子带划分；在各子带内联合多线性约束最小方差(Multiple Linear Constrained Minimum Variance, MLCMV)准则与零陷展宽算法求解鲁棒权矢量；构建小相位扰动模型，引入变步长SOCP算法求解唯相位权矢量；最后通过综合滤波器进行子带综合，形成宽带发射波束。仿真结果表明，该算法可有效展宽干扰零陷，显著提高收敛性，且易于工程实施。     

Salt-and-Pepper noise removal by median-type noise detectors and detail-preserving regularization.

This paper proposes a two-phase scheme for removing salt-and-pepper impulse noise. In the first phase, an adaptive median filter is used to identify pixels which are likely to be contaminated by noise (noise candidates). In the second phase, the image is restored using a specialized regularization method that applies only to those selected noise candidates. In terms of edge preservation and noise suppression, our restored images show a significant improvement compared to those restored by using just nonlinear filters or regularization methods only. Our scheme can remove salt-and-pepper-noise with a noise level as high as 90%.     

自适应噪声抑制器的设计和Simulink验证

在测井信号的处理中,经常会遇到非平稳噪声环境下的信号检测问题,此时很难用经典的滤波器系数固定的FIR滤波器或IIR滤波器来解决噪声背景下的信号提取问题。本文首先介绍了一种系数可变的FIR滤波器实现一种不需要参考噪声的自适应噪声抑制器的基本原理,然后在此基础上阐述了在Simulink环境中建模的具体方法,最后使用该模型对一个非平稳有色噪声信号进行抑制,仿真结果表明在不需要参考噪声源时通过该自适应噪声抑制器同样可以获得比较好的噪声抑制效果。     

自适应滤波在激光雷达中的应用

针对存在斑纹和湍流这两类乘性噪声的激光雷达系统,讨论了对数回波功率的估计问题。在模型参数未知的情况下,在自适应滤波中采用分割法,并给出了自适应滤波方程。仿真结果表明,在同时存在斑纹和湍流噪声的情况下,本文给出的自适应滤波算法优于只考虑斑纹噪声的滤波算法。     

一种基于参数自适应引导滤波的红外图像细节增强算法

图像分层滤波器中引导滤波器因其滤波保边效果好和计算复杂度低,在红外图像细节增强领域得到了广泛的研究与应用。但传统的引导滤波器固定的正则化参数ε不能在所有场景下都取得较好的滤波分层效果,所以本文提出基于局部方差的参数ε自适应算法,以提高引导滤波器场景适应性。此外本文进一步通过自适应参数ε值,提出了改进的基于噪声掩膜函数的细节层自适应增强算法,从而在有效抑制了图像噪声水平同时提高了算法在不同场景下的细节增强能力。     

泊松噪声模糊图像的边缘保持变分复原算法

从贝叶斯估计出发,构造了一种新的变分模型,用于复原被泊松噪声污染的模糊图像.首先讨论了模型正则化项中具有边缘保持能力的函数选取以及模型求解的相关问题,然后将变分模型的求解转化为可快速求解的非线性扩散方程,给出了正则化参数选取的初步空间自适应方法,可以区分平滑区域和图像边缘自适应的调节参数.实验结果表明,本文方法的复原效果整体上优于传统的迭代正则化方法,复原图像的边缘得到了有效的保护,泊松噪声的抑制效果明显,复原图像提高的改进信噪比(ISNR)要比迭代正则化方法平均提高1 dB以上.     

Bayesian regularization and nonnegative deconvolution for room impulse response estimation

This paper proposes Bayesian Regularization And Nonnegative Deconvolution (BRAND) for accurately and robustly estimating acoustic room impulse responses for applications such as time-delay estimation and echo cancellation. Similar to conventional deconvolution methods, BRAND estimates the coefficients of convolutive finite-impulse-response (FIR) filters using least-square optimization. However, BRAND exploits the nonnegative, sparse structure of acoustic room impulse responses with nonnegativity constraints and L/sub 1/-norm sparsity regularization on the filter coefficients. The optimization problem is modeled within the context of a probabilistic Bayesian framework, and expectation-maximization (EM) is used to derive efficient update rules for estimating the optimal regularization parameters. BRAND is demonstrated on two representative examples, subsample time-delay estimation in reverberant environments and acoustic echo cancellation. The results presented in this paper show the advantages of BRAND in high temporal resolution and robustness to ambient noise compared with other conventional techniques.     

Adaptive two-pass rank order filter to remove impulse noise in highly corrupted images

In this paper, we present an adaptive two-pass rank order filter to remove impulse noise in highly corrupted images. When the noise ratio is high, rank order filters, such as the median filter for example, can produce unsatisfactory results. Better results can be obtained by applying the filter twice, which we call two-pass filtering. To further improve the performance, we develop an adaptive two-pass rank order filter. Between the passes of filtering, an adaptive process is used to detect irregularities in the spatial distribution of the estimated impulse noise. The adaptive process then selectively replaces some pixels changed by the first pass of filtering with their original observed pixel values. These pixels are then kept unchanged during the second filtering. In combination, the adaptive process and the second filter eliminate more impulse noise and restore some pixels that are mistakenly altered by the first filtering. As a final result, the reconstructed image maintains a higher degree of fidelity and has a smaller amount of noise. The idea of adaptive two-pass processing can be applied to many rank order filters, such as a center-weighted median filter (CWMF), adaptive CWMF, lower-upper-middle filter, and soft-decision rank-order-mean filter. Results from computer simulations are used to demonstrate the performance of this type of adaptation using a number of basic rank order filters.     

A new method for adaptive IIR filter design based on tabu search algorithm

Adaptive digital filters have proven their worth in a wide range of applications such as channel equalisation, noise reduction, echo cancelling, and system identification. These filters can be broadly classified into two groups: finite impulse–response (FIR) and infinite impulse–response (IIR) filters. IIR filters have become the target of increasing interest because these filters can reduce the filter order significantly as compared to FIR filters. Tabu search is a heuristic optimisation algorithm which has been originally developed for combinatorial optimisation problems. It simulates the general rules of intelligent problem solving and has the ability of discovering the global minima in a multi-modal search space. In this work, a novel method based on tabu search is described for the design of adaptive IIR filters.