基于频率调制连续波的干涉型光纤声传感器信号检测技术

针对光纤干涉仪存在的相位衰落问题,讨论了基于正弦波形的频率调制连续波(FMCW)和锯齿波FMCW两种干涉型光纤声传感器的信号检测原理,并给出了数字化信号解调方案。采用7.5 m臂差的光纤声传感器和具备调频功能的半导体激光器,对10 kHz正弦波FMCW和锯齿波FMCW两种调频方式进行了信号解调实验对比。解调结果表明,两种调制解调方案均能稳定检测出加在声传感器上的模拟声信号,消除了光纤干涉仪的相位衰落。但在实际工程应用中,与正弦波FMCW检测方案相比,锯齿波FMCW方案无需检测混频信号的初始相位,检测灵敏度高且稳定,算法实现更为简单,而且使用不同臂差的光纤传感器可实现传感器的频分复用。     

新型数字式扫频噪声源的实现

给出一种数字式宽带扫频加窄带噪声调频噪声源的研究结果。该噪声源主要是采用单片机、高速数模转换器以及压控振荡器来实现。提出了一种新型三角波扫频的方式。通过对三角波扫频理论分析,阐明了新扫频方式比传统的锯齿波扫频速度更快,扫频频谱谱线间隔宽2~(1/2)倍的优点。在讨论了最佳干扰效果的基础上,给出了实现干扰源的硬件结构和干扰测试的分析结果。     

宽带雷达信号调制模块设计与实现

现代宽带高分辨雷达为适用各种复杂的环境,要求其信号调制方式具有多样化和灵活性。采用单片机串口通信接收命令,FPGA高速配置DDS和射频调相器,实现了一种支持连续波调频调相和脉内调频调相的雷达信号调制模块。通过代码控制解决了脉内调频较难同步的技术难题。整个调制模块载波中心频率为1.6 GHz,调频波形有正、负锯齿波和三角波,调频带宽可达600 MHz,调相序列有巴克码、伪码L和M序列。测试结果表明,系统输出信号稳定,可靠性高。     

基于FPGA的锯齿波调频梳状信号发生器的设计与实现

随着通信技术的发展,跳频信号在通信系统中有着越来越广泛的应用。梳状谱信号对跳频通信系统的影响十分显著,本文根据梳状谱信号产生机理,在理论分析与Matlab仿真基础上,基于FPGA平台设计与实现了锯齿波调频梳状谱信号发生器。     

基于锯齿波信号控制的扫频信号发生电路的设计

为将示波器用于网络的幅频特性测试中，用集成函数发生器ICL8038设计成由锯齿波电压控制的扫频信号发生器，并通过实验测量给出了电路元件参数、频率调节范围和电压与频率的对应关系，该扫频发生器中心频率范围是10Hz-600kHz范围，调节电压小于0．2v／kHz。     

MSM激光距离成像雷达中线性调频信号的产生

MSM激光距离成像雷达将微波雷达中调频测距原理应用到激光雷达,采用一个线性调频的射频副载波调制激光输出强度。本文提出了雷达中调频信号发生器产生宽带线性调频信号的设计思路,并着重阐述DDS+PLL实现宽带线性调频的过程。首先建立四阶二型锁相环架构,分析其对DDS产生的线性扫频信号实现频率稳态跟踪的可能性,然后进行锁相环锁频的初步仿真并得到环路基本参数,最后在输入DDS产生的线性扫频信号前提下仿真环路的频率跟踪性能。仿真结果表明四阶二型锁相环实现了对DDS产生的线性扫频信号的频率跟踪及频带扩展,满足了雷达距离精度对线性调频信号的宽频带要求。     

一种有效的线性调频雷达干扰技术

根据线性调频雷达发射信号的特点,提出一种新的干扰技术——锯齿波调频干扰,即干扰机对接收到的雷达照射信号附加锯齿波频率调制,将调频结果放大之后转发出去。根据调制参数不同,这种干扰可以产生假目标欺骗和覆盖干扰。该技术要求较低的干扰功率。理论分析和计算仿真验证了这一新技术的有效性。     

连续波调频引信信号的检测方法

以三角波调频无线电引信为对象,分析了调频引信的信号特征,针对低信噪比宽带连续波线性调频信号的检测,提出了基于延迟相乘的频带压缩检测算法.通过仿真试验验证,该算法对负信噪比宽带线性调频信号检测有较高的处理增益,且处理简单,工程可实现性好,满足对无线电引信信号高截获处理要求.     

DTMF信号解码抗语音干扰研究

DTMF信号具有优良的抗干扰性能和一定的抗多普勒频移的能力,广泛应用于语音信道的低速数据通信.文中论述了基于Goertzel算法的DTMF信号解码基本原理和基本过程,简要介绍了语音信号段的合成,在语音噪声背景下,仿真了Goertzel算法的DTMF信号解码情况.该算法只计算16个频点的信号幅值,得出了在语音背景下,信噪比高于8 dB情况时,基本可实现DTMF信号的高可靠检测.     

锯齿调频连续波建模与数值仿真

锯齿调频连续波由于测距精度高,在测距系统中有着广泛的应用.为了描述锯齿调频连续波多个调频周期中的频率和相位变化及跨越调频周期时的不连续性,结合具有一定周期性和不连续性的模数函数,推导了锯齿调频连续波的频率和相位的数学表达式,并进行建模及仿真.通过对频率与相位的微积分关系的推导解析地验证了数学表达式的正确性.数值仿真结果也表明:频率和相位的数学表达式满足微积分的关系,是频率与相位的正确的表达式,能够描述锯齿波调频在跨越调频周期时的不连续性.提出的锯齿调频连续波的多调频周期频率与相位表达式可为进一步研究锯齿调频连续波测距过程提供基础.     

飞机遇险呼救还是干扰信号?

121．5MHz是国际电联规定的飞机遇险呼救专用频率，其他任何部门和个人不得占用。一旦出现呼救信号，必须立即找到信号源．否则后果不堪设想。     

Le traitement du signal vocal voice signal processing

The speech processing studies have advanced rapidly in recent years spurred on by great progresses in thevlsi technologies and in the digitalization of the networks. This paper offers an overview of the most attractive techniques which have focused the recent researchs and developments in speech coding, recognition and synthesis areas. For speech compression, the emphasis is put on a family of techniques named code-excited linear prediction (celp) which dominates current studies for rates in the range of 4 to 16 kbit/s. In terms of speech recognition, particular emphasis is placed on the following three elements which are essential in order to increase the robustness of the systems : telephone line adaptation, rejection of parasite noise and out-of-vocabulary words, and keyword spotting. In terms of text-to-speech synthesis, thepsola (pitch synchronous overlap and add) technique is outlined herein. This technique gives rise to a new generation of synthesis systems which produce speech with very natural timbre. The analysis of current tendencies for each area allows to suggest attractive directions for future research.     

双通道矢量信号分析仪测试系统延迟特性

使用双通道分析仪表测试两路相位相参的调制信号间的时间关系．一方面验证使用互相关技术方法测试信号间时间关系的可行性．另一方面通过对被测电路器件的输出和输入信号的相关测试．得到电路在复杂激励状态下的延迟特性。[编者按]     

基于矢量信号发生器的微波着陆信号模拟

针对当前微波着陆装备测试的迫切需要,提出一种基于矢量信号发生器硬件架构的微波着陆模拟器的实现方式,并提出了一种基于FPGA的微波着陆系统基带信号设计方法。对微波着陆信号的格式和信号时序进行了仿真,并对各种角功能信号和数据字结构进行了实际测试,证明了该模拟器性能达到装备测试要求。     

集成语音信号与表面肌电信号的情感自动识别模型

为了提高情感识别的正确率,针对单一语音信号特征和表面肌电信号特征存在的局限性,提出了一种集成语音信号特征和表面肌电信号特征的情感自动识别模型.首先对语音信号和表面肌电信号进行预处理,并分别提取相关的语音信号和表面肌电信号特征,然后采用支持向量机对语音信号和表面肌电信号特征进行学习,分别建立相应的情感分类器,得到相应的识别结果,最后将识别结果分别输入到支持向量机确定两种特征的权重系数,从而得到最终的情感识别结果.两个标准语情感数据库的仿真结果表明,相对于其它情感识别模型,本文模型大幅提高了情感识别的正确率,人机交互情感识别系统提供了一种新的研究工具.     

Multiple digital signal processor environment for intelligent signal processing

We describe a novel, expandable, multiple digital signal processor (DSP) architecture with a symbolic processing host. A multiprocessor board, called Odyssey, based on this architecture has been developed to combine symbolic and real-time digital signal processing in a single computing environment. Some of the key features of the board are: 20 million multiply/accumulates per second, 512K bytes of data space, and expandability to 16 boards on a NuBus host. The DSPs used are the TMS32020 signal processing chips developed by Texas Instruments, and the host is Texas Instruments' Explorer, a LISP machine workstation. This provides environment to perform many intelligent signal processing tasks by associating meaningful relationships between quantitative (signal processing) and qualitative (symbolic processing) entities to develop inferences using expert system technology. Applications such as grammar-driven connected speech recognition, neural network simulation, EEG analysis, and generation of speech from general English text with natural language processing are some of the tasks that can utilize the computational power of the multiple DSP and/or the associated symbolic processing capabilities. Software development tools to implement applications include the device driver to facilitate communication between the host processor and the Odyssey board, a unique window-based debugger resident on the Explorer that allows for simultaneous state display of all the processors on the board, a FORTH interpreter for high-level language programming, and a cross-assembler/linker for assembly level programming.     

Adaptive machine learning algorithm employed statistical signal processing for classification of ECG signal and myoelectric signal

Multidimensional Systems and Signal Processing - In this research paper we present designing and evaluating the electrocardiography (ECG) and Myoelectric signal (EMG) pattern recognition methods...     

Resonance-based signal decomposition: A new sparsity-enabled signal analysis method

Numerous signals arising from physiological and physical processes, in addition to being non-stationary, are moreover a mixture of sustained oscillations and non-oscillatory transients that are difficult to disentangle by linear methods. Examples of such signals include speech, biomedical, and geophysical signals. Therefore, this paper describes a new nonlinear signal analysis method based on signal resonance, rather than on frequency or scale, as provided by the Fourier and wavelet transforms. This method expresses a signal as the sum of a ‘high-resonance’ and a ‘low-resonance’ component—a high-resonance component being a signal consisting of multiple simultaneous sustained oscillations; a low-resonance component being a signal consisting of non-oscillatory transients of unspecified shape and duration. The resonance-based signal decomposition algorithm presented in this paper utilizes sparse signal representations, morphological component analysis, and constant-Q (wavelet) transforms with adjustable Q-factor.     

基于信号子空间的直接序列扩频信号波形估计方法

将直接序列扩频(DS-SS)信号波形估计问题归结为信号子空间估计问题,提出基于传播算子算法的扩频波形估计方法。针对特征值分解求解信号子空间计算量较大的问题,利用传播算子估计信号子空间,并对算法计算量和性能进行理论分析和实验验证,结果表明该算法有效,且计算量远小于现有算法。