有源微波冷噪声源的原理及其应用分析

有源微波冷噪声源是一种新型微波噪声源部件,其以场效应管作为核心噪声发生元件,能够在常温环境下输出极低的噪声温度,是为微波辐射计提供低温定标参考点的理想装置。在介绍国内外有源微波冷噪声源技术发展现状和特点的基础上,分析了有源微波冷噪声源的工作原理和关键技术。提出了一种有源微波冷噪声源设计方法,重点分析了设计过程中的关键环节对噪声源性能产生的影响。采用pHEMT型场效应管研制了L波段有源微波冷噪声源器件,并给出了有源微波冷噪声源作为微波辐射计噪声源的应用方案。     

深亚微米工艺下物理噪声源的瞬态噪声分析

物理噪声源在密码设备研制、密钥产生以及通信信息保护等很多领域有着广泛应用。针对物理噪声源为通信系统最重要的性能指标,噪声源的随机性检验通常都是在流片后对样品测试完成的。在物理噪声源设计过程中首次引入瞬态噪声分析方法对随机性进行仿真,即把频域噪声模型转化到时域,在时域瞬态仿真中加入噪声的影响。实现了在设计过程中就可以对噪声源随机性进行仿真分析。在给出了噪声源电路结构之后,使用Cadence Spectre仿真工具和GPDK090 90nm工艺设计包,对模型产生的数据序列进行了随机性分析,结果表明随机性满足FIPS140-2检验标准。     

盲编码与有信编码结合的音频扩谱水印技术

水印系统通常被看作为一种通信模型,水印相当于被传送的信息,宿主信号为信道第一噪声源,攻击为信道第二噪声源。有信编码可以抵制第一噪声源对水印的干扰,而盲编码可以抵制第二噪声源的干扰。文章尝试性地提出了盲编码与有信编码结合的自适应的音频扩谱水印算法,用有信编码来抵制第一噪声源对水印的干扰,用较高码率的盲编码抵制第二噪声源的干扰。通过大量的实验证明,与其它的水印编码方案相比[3,4],该文的方法在多种攻击测试中具有很强的鲁棒性,其结果接近脏纸理论[7]的理想性能。     

开关电源变换器传导电磁干扰噪声源内阻抗优化提取方法

针对开关电源变换器(Switch mode power supply,SMPS)传导电磁干扰噪声源内阻抗提取问题,本文基于传统电压插入损耗法提出了一种功率插入损耗噪声源内阻抗提取方法。根据测试系统在被测噪声源加载前后功率损耗原理,通过校准测试系统自身的功率插入损耗,得到被测设备噪声源内阻抗。本文方法无需预估被测设备噪声源内阻抗,测试方法简便。理论研究与实验结果表明,本文方法进行快速、有效的提取被测设备噪声源内阻抗,从而为EMI滤波器设计提供理论依据。     

光纤陀螺信号误差分析与滤波算法的研究

针对光纤陀螺信号漂移误差和噪声的影响,采用Allan方差法对光纤陀螺的各项随机误差成分进行了分离和计算.然后结合陀螺稳定平台系统研究了滑动滤波、小波变换阈值滤波两种直接对陀螺输出信号进行数字滤波处理的方案.最后对某陀螺惯性稳定跟踪转台中使用的光纤陀螺信号的测试和统计分析结果表明,采用Allan方差法能够有效地分离和辨识陀螺零漂信号中的各项噪声源随机误差系数和误差大小,采用的小波变换阈值滤波的去噪效果明显.     

可增量学习的水下航行器噪声源识别中聚类算法研究

水下航行器的噪声源识别具有训练样本有限,存在偶发或突变噪声源等特点。本文针对这些特点,在具有增量学习能力的水下航行器的噪声源识别系统架构下,提出了一种参数自适应可调的基于密度的聚类算法。实验表明,该算法可以有效避免基于密度的聚类算法的参数敏感性对聚类结果的不良影响,在无监督情况下对水下航行器的机械噪声源样本进行有效聚类。通过该聚类算法标注后的样本可直接作为具有增量学习结构的分类器的训练样本,节省了时间和系统开销。     

电子电路在噪声源处抑制噪音的措施

在电子电路中如果能找到噪声源,采取相应措施在噪声源处将噪声抑制掉这是最彻底最有效的解决办法。然而,由于噪声源一般不直观,人们常常忙于在受干扰处采取各种措施,结果却不很理想。所以首先要找到噪声源,然后采取相应措施加以抑制。有的噪声源是很明显的,例如雷电、广播电台的发射、电网上大功率设备的运行等。不过,像在这种噪声源处是不大可能采取什么抑制措施的。然而,一般的电子电路中许多的噪声源是完全可以采取各种相应措施的,问题在于往往不清楚噪声源是什么,在什么地方。寻找噪声源也有一个原则,那就是电源和电压发生剧变的地方一般就是电子电路的干扰源。     

混合噪声中特定噪声源定位方法研究

研究噪声源准确定位问题。针对当需要检测位置的噪声源处在多噪声发声的混合环境中,多个噪声强度特征信息发生混合,造成特定噪声强度特征信息混淆和丢失,传统的噪声特征定位的检测方法无法准确定位单个噪声源,为了解决准确定位,提出了混合声强度的多噪声源分割定位方法。通过建立声强度模型,然后提取声音强度信息,计算声强度的声源归属度,再以具有最大归属值的声强度作为当前分布声源来描述声音的特征,混合噪声中特定噪声源定位。通过验证表明能有效的定位混合噪声环境下的特定噪声源,取得了比较好的识别效果。     

基于频谱与小波变换的工业压缩机噪声源分析仪设计

文章介绍了一套基于频谱与小波变换的工业压缩机噪声源分析仪的设计。该分析仪以LabVIEW为软件开发平台,采用频谱分析法和连续小波变换法对工业压缩机的噪声源进行定位识别分析,具有噪声信号采集、处理、频谱分析法识别以及连续小波变换法识别等功能。试验结果表明,这2种识别方法均能区分出工业压缩机的各个噪声源及其在压缩机噪声声功率中的贡献,便于迅速、直观地定位主噪声源的频带分布范围,进而监测工业压缩机的运行状态。     

Analysis and improvement of PSRR in an envelope‐tracking power amplifier using a common‐drain amplifier topology

A common‐drain power amplifier (PA) for envelope tracking systems is presented. In envelope tracking, the main PA operates mostly in compression and the power supply rejection ratio (PSRR) is not high. Furthermore, the output noise of the supply modulator can be mixed with the RF signal and generates out‐of‐band emissions. In this article, instead of using a common‐source topology, the PSRR of the envelope tracking PA is inherently improved by utilizing a common‐drain topology. A comprehensive analysis shows that the common‐drain topology is less sensitive to the supply noise, as compared to the conventional common‐source topology. The proposed common‐drain PA is implemented using a discrete LDMOS PD20010‐E RF transistor. Measurement results show that the PSRR of the proposed common‐drain PA is improved by up to 7 dB as compared to that of the common‐source PA. For a two‐tone input with 10 MHz bandwidth at the center frequency of 700 MHz, the power added efficiency (PAE) and IM3 of the envelope tracking common‐drain PA are 20% and ? 28 dBc, respectively, at an average output power of 33.4 dBm. The amplifier also shows a 12.4 dB power gain. Moreover, by utilizing the envelope tracking, the PAE is improved by more than 5%.     

基于FastICA的雷达弱目标检测前跟踪方法研究

研究了强噪声干扰下的雷达弱目标检测及跟踪问题。针对信号与噪声干扰之间的相互独立性,提出了一种基于FastICA的弱目标检测前跟踪(TBD)算法。该算法的关键为对分离后的信号进行能量分配,以及求解所构建的一个多目标规划问题,进而实现匹配回波信号的能量积累。仿真实验结果表明,在负信噪比的情况下,无论是高斯噪声还是非高斯噪声,该方法均可以实现检测前跟踪的目的;用能量积累过程中记录的目标状态信息为观测值,则可以通过滤波估计的方法最终实现对目标的稳健跟踪。该方法为弱小目标的跟踪检测技术提供了一种新的思路。     

基于盲源分离和噪声抑制的语音信号识别

为了更准确地在噪声环境中对不同语音信号进行识别,提出了一种用于普适语音环境下的自优化语音活动检测(VAD)算法,该算法运用个性化语音命令自动识别系统的语音信号,并能够有效地从多个发声者的混合语音中分离出个体发声者的声音,通过跟踪语音功率谱的较高幅度部分和自适应地抑制噪声来检测发声者的语音信号;设计并实现了一种处理多个发声者任务的自动语音识别(ASR),免去了对干净的语音变化进行先验估计,直接利用噪声本身产生语音/非语音判决的阈值以完成自优化过程;使用语音数据库NOIZEUS进行了评价测试,实验结果表明,所提出的盲源分离和噪声抑制方法不需要任何额外的计算过程,有效地减少了计算负担。     

A new efficient backward BSS crosstalk-resistant algorithm for automatic blind speech quality enhancement

In last 10 years, several noise reduction (NR) algorithms have been proposed to be combined with the blind source separation techniques to separate speech and noise signals from blind noisy observations. More often, techniques use voice activity detector (VAD) systems for the optimal solution. In this paper, we propose a new backward blind source separation (BBSS) structure that uses the input correlation properties to provide: (i) high convergence rates and good tracking capabilities, since the acoustic environments imply long and time-variant noise paths, and (ii) low misalignment and robustness against different noise type variations and double-talk. The proposed algorithm has an automatic behavior to enhance noisy speech signals, and do not need any VAD systems to separate speech and noise signals. The obtained results in terms of several objective criteria show the good performance properties of the proposed algorithm in comparison with state-of-the-art algorithms.     

在线提高卡尔曼滤波跟踪精度的参数研究

为了在线提高卡尔曼滤波算法（KF）的跟踪精度,对随机序列进行高斯度的定义,将随机序列的分布分为超高斯、高斯、次高斯和非高斯,找出KF可以工作的范围。针对噪声服从次高斯分布时,KF跟踪精度不高,介绍了两个可以判断KF使用过程中设定的噪声协方差与实际是否一致的参数。提出了参数自适应的方法,使设定的噪声协方差与实际值可以自适应地一致,从而提高了KF的跟踪精度。实验结果表明,噪声服从高斯和超高斯分布时,KF跟踪精度很高;噪声服从次高斯分布时,若噪声协方差的设定值与实际值不一致,跟踪误差会很大,对此使用了参数自适应法,可以大大提高KF的跟踪精度。     

Noise Tracking Using DFT Domain Subspace Decompositions

All discrete Fourier transform (DFT) domain-based speech enhancement gain functions rely on knowledge of the noise power spectral density (PSD). Since the noise PSD is unknown in advance, estimation from the noisy speech signal is necessary. An overestimation of the noise PSD will lead to a loss in speech quality, while an underestimation will lead to an unnecessary high level of residual noise. We present a novel approach for noise tracking, which updates the noise PSD for each DFT coefficient in the presence of both speech and noise. This method is based on the eigenvalue decomposition of correlation matrices that are constructed from time series of noisy DFT coefficients. The presented method is very well capable of tracking gradually changing noise types. In comparison to state-of-the-art noise tracking algorithms the proposed method reduces the estimation error between the estimated and the true noise PSD. In combination with an enhancement system the proposed method improves the segmental SNR with several decibels for gradually changing noise types. Listening experiments show that the proposed system is preferred over the state-of-the-art noise tracking algorithm.     

基于锁相合成方式的宽带chirp信号发生器

本文基于对传统线性调频信号源解决方案的分析,提出了一种采用锁相环实时跟踪DDS的方案。该设计的输出频率为740MHz到1060MHz。相比于传统方案,该方案的优点是:高线性度,高稳定性,高杂散抑制和低相位噪声。     

An on-line shape-matching weld seam tracking system

Automatic welding technology continues to find a broader application in diverse industries due to its high efficiency and accuracy. In this work, an on-line laser-based machine vision system for seam tracking was developed. To achieve a reliable and accurate seam tracking process that is adaptive to different groove types, a shape-matching algorithm was proposed and implemented. The algorithm uses the previous groove shape as the template to locate the next groove shape. Tests on U-groove, tap-groove and free form groove have verified its adaptability and robustness to different groove types with noise. The shape-matching algorithm also enables the seam tracking system to automatically localize the starting and finishing point of the weld seam. A FIFO based queue was defined and implemented to tackle the lag distance problem between the heat source and the vision sensor. The tracking algorithm, along with the FIFO queue was successfully verified on a sine-shaped seam. A tracking accuracy of ±0.5 mm was achieved in this test, which is acceptable in most of the arc welding applications.     

Sound and Visual Tracking for Humanoid Robot

Mobile robots capable of auditory perception usually adopt the stop-perceive-act principle to avoid sounds made during moving due to motor noise. Although this principle reduces the complexity of the problems involved in auditory processing for mobile robots, it restricts their capabilities of auditory processing. In this paper, sound and visual tracking are investigated to compensate each other's drawbacks in tracking objects and to attain robust object tracking. Visual tracking may be difficult in case of occlusion, while sound tracking may be ambiguous in localization due to the nature of auditory processing. For this purpose, we present an active audition system for humanoid robot. The audition system of the highly intelligent humanoid requires localization of sound sources and identification of meanings of the sound in the auditory scene. The active audition reported in this paper focuses on improved sound source tracking by integrating audition, vision, and motor control. Given the multiple sound sources in the auditory scene, SIG the humanoid actively moves its head to improve localization by aligning microphones orthogonal to the sound source and by capturing the possible sound sources by vision. The system adaptively cancels motor noises using motor control signals. The experimental result demonstrates the effectiveness of sound and visual tracking.     

闪烁噪声下基于模型集合切换的机动目标跟踪算法

在目标跟踪过程中,由于目标与传感器相对位置的变化以及目标不同部位的反射干扰,传感器测量数据中的测量噪声不再是严格意义上的高斯噪声,而变为具有长拖尾特性的闪烁噪声,而闪烁噪声的出现将严重影响线性卡尔曼滤波器的滤波性能。机动目标跟踪算法应同时考虑目标运动模式变化和闪烁噪声出现对滤波过程的影响。将滤波方程集合划分为包含不同目标运动模式的模型集合,提出了基于模型集合切换的机动目标跟踪(MSSM)算法。仿真结果验证了MSSM算法的有效性。