基于幅度谱的椒盐噪声估计

提出了一种基于幅度谱的椒盐噪声估计方法,该方法利用图像的幅度谱随椒盐噪声密度大小的变化规律,对图像中的椒盐噪声进行了估计.通过对大量含噪图像进行实验结果表明:该方法在对椒盐噪声密度中等和较高时效果较好.     

A Modified Borderline Smote with Noise Reduction in Imbalanced Datasets

In this paper, a co-design of a dual-band low-noise amplifier (DB-LNA) with a dual-band band-pass filter (DB-BPF) for a radio navigation aid (RNA) application was proposed. The novel development was that the DB-LNA was directly integrated with the DB-BPF instead of connecting the output matching network (OMN) of the DB-LNA to the 50 Ω-port of the DB-BPF. Thus, this DB-BPF had a double function, serving as the DB-BPF and also as the OMN. This architecture was called the co-design structure. Z_IN analysis was used to evaluate the co-design network structure. In general, the design procedure was divided into four sections, including (1) DB-BPF, (2) DB-LNA, (3) Cascade DB-LNA and DB-BPF, and (4) Co-design DB-LNA and DB-BPF. The co-design method was applied in an RNA implementation at dual-band frequencies of 113 MHz and 332 MHz. Validation of the proposed structure is confirmed for its accuracy by simulating the impedance characteristic Z_IN, S parameter simulation, and measurement results. The key contributions of this paper were that: (1) The co-design structure could reduce the passive component by 31.5%, (2) the total size of the DB-LNA and DB-BPF using the co-design method was smaller than the cascaded method by 11.36%, (3) more light-weight in fabrication due to a smaller size, and (4) finally, the proposed LNA has a higher figure of merit than the other LNA.

     

干涉图幅度统计分布模型族及其参数估计

提出了新的干涉图幅度分布模型族及对应的参数估计器.首先,在乘积模型框架下,以复Wishart分布为基础,通过引入场景环境分类的思想,利用具备真实地物RCS分量精确建模能力的逆方根Gamma分布和广义逆Gauss方根分布,推导建立了理论完善的新的干涉图幅度分布模型族:E-Gamma分布、E-G0分布和E-G分布,解决了均匀度变化下的广泛区域对应的干涉图幅度数据的精确建模问题,实现了单通道SAR图像建模理论到多通道SAR干涉图幅度建模的拓展与统一;其次,基于Mellin变换导出了各新分布模型的参数估计器:E-Gamma_MoLC、E-G0_MoLC和E-G_MoLC,新估计器能够快速、准确地迭代出各分布参数的估计值.以KL度量、MSE度量和K-S检验作为定量评估准则,对星载多通道SAR实测数据对应的干涉图进行实验,结果证明了所提模型族及对应参数估计器的有效性.     

相位噪声对误差向量幅度的影响

对本振源相位噪声与直接变频发射机误差向量(EVM)之间的关系进行了分析,综合考虑了测试电路中升余弦滤波器的影响,在锁相环路(PLL)建模方面引入了峰值相噪以及噪声底的因素.通过Matlab仿真,给出了部分典型情况下本振源相位噪声与EVM之间的对应关系曲线,仿真结果表明峰值噪声和升余弦滤波器的确对直接变频发射机的误差向量有不可忽略的较大影响,从而大大提高了EVM估算的精度.     

Robust Noise Estimation Based on Noise Injection

Noise estimation is an important premise for image denoising and many other image processing applications, and related research has drawn increasing attention and interest. In this paper, a novel noise level estimation algorithm is proposed by investigating the distribution of local variances in natural images. There are two major contributions of this work to tackle with the challenges in noise estimation: 1) a wavelet decomposition based preliminary estimation stage to alleviate the influence of image’s textural or structural information; 2) a noise injection based estimation stage to simulate the impact of noise-free image content on the variance distribution, which otherwise almost always leads to overestimation. The cascade scheme of this two-step estimation procedure can reduce the detrimental influence of textural image regions effectively and therefore relieves overestimation of the noise variance. Moreover, the proposed method is not limited to any specific type of noise distribution. Extensive experiments and comparative analysis demonstrate that the proposed algorithm can reliably infer noise levels and has robust performance over a wide range of visual content, as compared to relevant methods.     

非平稳噪声环境下的噪声估计算法

通过对噪音和语音频谱的分析,针对航空背景噪声的特性,提出一种用于语音增强的新的噪声估计算法。通常的噪声估计一般利用语音端点检测方法,取噪声段的谱平均值作为待估计的噪声谱,但该方法在信噪比较低时性能下降严重。笔者提出的基于频率段能量比的噪音谱估计方法,不依赖于语音端点检测而直接由语音帧来估计噪音谱,通过计算一帧语音中各频率段中能量比,以判断该帧是否含有语音来修正噪声谱估计的计算因子。算法提高了谱减法的适用范围,还在一般谱相减方法的基础上提出了改进的谱相减算法。     

Adaptive Noise Variance Estimation in BayesShrink

A method of noise variance estimation in BayesShrink image denoising is presented. The proposed approach competes with the well known MAD-based method and outperforms this method in more than 99% of our experimental results. The approach, called Residual Autocorrelation Power (RAP), provides a more accurate noise variance estimate and results in a smaller MSE.     

信道估计中基于变换域的降噪处理技术

本文讨论了信道估计中的降噪处理技术，根据信道中加性高斯噪声的特点，提出在变换城中抑制噪声能量的方法。大大提高了估计精度。文中分析了两种降噪算法，并从理论上分析了算法一的处理增益。仿真表明．经过降噪算法二处理后信道估计精度得到了大犬的提高。其估计误差时系统误码性能的影响已经极小，单纯靠提高信道估计精度已很难进一步改善系统的误码性能。     

一种基于噪声估计的彩色噪声滤除算法

在视频处理中，为了提高电视画面的清晰度，不但需要对亮度噪声进行滤除，对色度噪声的滤除也是必不可少的。文章的滤波算法是：先基于块操作对图像的噪声情况做一个大概的估计，然后根据估计的噪声情况对图像进行降噪或者不作处理。降噪的时候。对彩色脉冲和高斯噪声分别选择不同的阵噪算法。文章着重介绍了彩色脉冲噪声的滹除，该算法综合了线性和非线性矢量滤波，降噪效果明显,对高斯噪声采用矢量均值滤波。     

Noise reduction in MR echo planar image reconstruction

It is shown that the noise level in EPI (echo planar imaging) can increase very significantly if the final image is deconvolved to remove the ghost artifacts. This noise amplification is caused by divisions of small numbers, which scale the noise variance up. The scaling factor is a function of the frequency variable in the uniform sampling direction, and is small for low-frequency components and large for high-frequency components. A window function to provide an inverse scaling of data so that the noise scaling factor is canceled exactly is proposed. Since the signal energy is concentrated in the low-frequency range, it is not reduced significantly when windowing is applied. The window function is compared to the commonly used Hamming window and is shown to have good frequency response. The algorithm has been tested with computer simulations and has been verified to be able to raise the signal-to-noise ratio by 50% in the final image.     

一种空间非均匀噪声环境下的DOA估计

针对空间非均匀噪声环境下的DOA(波达方向)估计问题,在中心对称分布的非均匀线性阵列上应用改进的恢复噪声协方差算法[6]得到一种新的DOA估计方法。由于非均匀阵列的阵列孔径更大,因此该方法可以估计空间相距更近的独立信号,另外该方法使用了平滑预处理技术,因此可以估计包含一对相干信号的信源,且所需的分辨信噪比门限更低。利用仿真实验验证了该方法的有效性。     

FPGA在共模噪声能量评估中的应用

在电力电子开关变换器数字化共模噪声抑制系统中,一个重要环节就是共模噪声的评估。由于共模噪声电流振荡频率很高,基于DSP的采样控制很难满足要求。文章提出了用FPGA作为主要器件、以能量为指标的共模噪声评估技术。所提方案中,FPGA可完成对高速AD芯片的采样控制、读取采样值、计算共模电流的噪声能量值并将该值反馈给DSP。DSP也可以通过向FPGA写入控制字和发启动脉冲等方式对噪声电流的采样过程进行宏观层面的控制。     

高斯噪声中的参数盲估计

盲信号处理方法中常忽略噪声的影响,而实际问题中噪声的影响是存在的.本文主要讨论了在协方差矩阵未知的加性高斯噪声中混合系数的盲估计问题.本文以最大似然估计为基础,提出一种求解参数的最优化算法,给出了混合矩阵和协方差矩阵的计算式.采用高斯混合模型(GMM)来逼近源信号的概率密度函数,简化了算法中的积分,导出了一种基于EM算法的迭代式.仿真表明,算法不仅能稳定收敛,而且在低信噪比下也能获得良好性能.     

脉冲噪声下的时间延迟估计方法研究

针对室内脉冲噪声居多的情况,提出了一种根据噪声的分布特性,选用不同算法来实现时延估计.这里时延估计算法包括基于分数低阶和二阶统计量的方法.由于脉冲噪声服从低阶分布,采用分数低阶协方差方法.可以得到较好的时延估计精度及良好抗噪声能力和抗混响能力,比较适合室内的语音源定位.     

基于EM算法的非高斯噪声参数估计

EM算法是一种从＂不完全数据＂中求解模型参数的极大似然估计的方法,在非高斯噪声的参数估计问题中是一种比较优秀的算法。非高斯噪声的参数估计问题的主要困难是充分统计量是不存在的,这意味着从观测空间到估计空间的映射依赖于这里试图估计的参数。在未知噪声概率密度的情况下,EM算法可以更准确地对非高斯噪声参数进行估计,估计方差接近C-R下界。     

冲击噪声背景下宽频段信号的多维参数估计

提出了一种基于协变异矩阵的宽频段信号频率、二维到达角和极化参数联合估计的算法.该算法利用非均匀L型极化敏感阵列的时空数据构造了一个时空协变异矩阵,通过对其进行特征分解,并利用分解得到的特征值和特征矢量估计出宽频段信号的多维参数.在冲击噪声环境下,该算法能够精确地估计信号的多维参数,无需多维谱峰搜索和参数配对过程,并具有较好的信源参数兼并能力.计算机仿真结果验证了算法的有效性.     

基于Screened Ratio原理的冲击噪声环境下DOA估计算法

该文提出了一种冲击噪声环境中DOA估计的算法。算法首先根据Screened ratio原理构造阵列信号的相关矩阵,然后利用MUSIC算法实现DOA估计。与基于分数低阶矩(FLOM)的算法相比,该文算法不需要选择FLOM参数p。计算机仿真表明该文算法在冲击噪声环境下具有更佳的稳定性和估计精度。     

Delay Estimation in the Presence of Timing Noise

We consider the problem of delay estimation in the presence of timing noise. We introduce an iterative algorithm with superior performance compared to the traditional method of using only cross-correlation. This method can exploit statistical knowledge of the timing noise such as loop bandwidth, giving further improvement in performance.      

基于共轭噪声组的宽带噪声雷达机动目标参数估计

宽带噪声雷达参数估计时通常会采用宽带互模糊函数的方法,但是在处理机动目标时这种方法需要在距离、速度及加速度3维搜索而导致运算量巨大,为此该文提出一种基于共轭噪声组的机动目标参数估计算法。该算法首先根据回波伸缩效应预设多路通道,每路通道截取固定长度的噪声组内信号进行混频,然后利用分数阶傅里叶变换(FrFT)估计混频信号的多普勒相位,根据相位信息构造补偿函数,并对补偿后的噪声组信号利用频域尺度相关(FSC)算法估计回波的时延,最后联立多普勒相位及时延信息获取目标的距离、速度和加速度。该算法避免了目标参数3维搜索的过程,无需时域重构回波信号,较宽带互模糊函数方法极大地降低了运算量,整个算法都可通过快速傅里叶变换(FFT)实现,便于系统实时处理。仿真结果验证了该算法的有效性及优势。