微波着陆系统信号模拟器设计

介绍了一种基于FPGA的微波着陆系统信号模拟器,采用基于函数发生器方式设计,由计算机根据微波着陆系统的信号格式计算各种信号数据,将信号数据灵活下载到存储器中,按照要求可产生正常或异常的信号及控制时序.实际测试和应用证明,信号模拟器的性能指标达到设计要求.该方法可应用于多种信号模拟器的设计.     

基于矢量信号发生器的MLS信号产生与校准研究

基于微波着陆信号特性,提出了应用矢量信号发生器的MLS信号产生方法,与传统微波着陆模拟器相比,信号参数调整更加灵活;同时,采用高速数字示波器、矢量信号分析仪等通用仪器,通过对微波着陆信号直接采样,实现了微波着陆角度及DPSK特性的校准,与传统方法相比,校准精度更加准确、可靠,确保微波着陆系统功能、性能测试。     

基于FPGA的微波着陆系统信号模拟器设计

为设计一种通用的无线电导航系统信号模拟器,同时实现微波着陆系统测试设备的小型化与数字化,采用软件无线电的思想,基于现场可编程门阵列(FPGA)设计了一种可编程实时微波着陆信号模拟器。模拟器以单片FPGA为主控芯片,利用串口与上位机软件通信,获取信号参数,并由FPGA生成数字基带信号,配合高性能数模转换器AD9777和宽带正交调制器ADL5375实现信号的模拟。对微波着陆信号的FPGA实现进行了全面的研究,对FPGA实现中难点问题的解决方法和采用的关键技术进行了详细介绍,并利用Modelsim 10.1a对设计进行了验证,证明设计达到预期效果。     

微波着陆系统内场模拟器射频电路设计

内场模拟器是重要的微波着陆系统专用测试信号源。根据有关指标要求,提出了模拟器的一种射频设计方案,该射频电路具有动态范围大、结构紧凑等特点,已在某新型微波着陆系统内场模拟器样机研制中得到成功应用。     

基于VSA的微波着陆模拟器角精度校准方法研究

随着矢量信号研究的不断深入和矢量信号分析仪器的广泛使用,基于微波着陆信号包括数字信号和模拟信号的特性,提出了矢量幅度误差与信噪比、相位噪声的数学关系,在微波着陆角精度分析基础上,通过测量模拟器输出信号相位噪声和数据信号 EVM 性能,计算扫描波束信噪比,实现模拟器不同输出信号角精度的标校。试验表明该方法简便、易行,满足微波着陆模拟器输出信号角精度的标校。     

应用矢量信号发生器产生MLS信号

本文简单介绍了微波着陆系统的原理，重点阐述了应用矢量信号发生器产生MLS信号的过程及实现方法，并对此信号进行了详细测试，这种方法与传统的MLS产生方法相比有许多明显的优势，不但可以简化MLS接收设备的调试，还可以进一步提高微波着陆系统的测试精度。     

基于人工神经网络的GPS卫星信号模拟器信号估计方法

多通道全球定位系统(GPS)卫星信号模拟器用来为GPS接收机和导航系统提供逼真的测试信号。该文从模拟器设计的角度对到达GPS接收机天线的卫星信号进行了分析,着重讨论了因众多误差因素影响而不易直接利用经验模型确定的几个波形参量的估计问题。基于人工神经网络(ANN)理论,提出一种利用ANN来模拟信号传播延迟、载波相位、信号功率等参量的方法。给出了基于ANN的模拟器闭环测试系统的结构。并对所设计的ANN进行了训练和验证,仿真实验结果表明,所设计的ANN能够在统计意义上逼真地模拟样本数据,从而使基于ANN的模拟器信号状态参量计算能够满足设计要求,可以直接应用于多通道GPS信号模拟器的研制。     

一种宽带LFMCW雷达信号模拟技术研究

提出了一种基于Agilent N8241A任意波形发生器和E8267D矢量信号发生器的宽带LFMCW雷达回波信号模拟器设计方案。该设计方案采用计算机产生LFMCW雷达信号波形数据,由N8241A通过DAC将其转化为宽带IQ基带信号,再经过E8267D进行IQ调制最终转换为微波射频信号输出,供实验室条件下宽带LFMCW雷达测试使用。实验结果表明,该模拟器具有良好的通用性和精确度,且结构简单。     

机载着陆设备信号模拟器的研制

通过分析仪表着陆系统（ILS）航向台、下滑台、指点信标台的信号格式,甚高频全向信标（VOR）台的信号格式,微波着陆系统（MLS）方位制导信号、仰角制导信号及基本数据信号的格式、性能参数等,构建满足基层级、中继级、基地级的着陆设备信号模拟器。模拟器采用数字信号处理（DSP/FPGA）、频率合成、矢量调制等技术,解决了低频信号合成和射频信号输出调整等问题。     

卫星导航信号模拟器现状与发展趋势

卫星导航信号模拟器是一种高精度的标准信号源,可以为导航接收机的研制开发、测试提供仿真环境。本文详细介绍并对比了软件模拟器和硬件模拟器这两种卫星导航信号模拟器的技术特点和现状,介绍了传统的基于模拟技术的硬件模拟器和当前的基于数字技术的模拟器的体系架构和性能特点。最后对模拟器未来的发展趋势做了探讨。     

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]     

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。     

数字信号处理及DSP芯片的发展前景

简要介绍了DSP的设计流程及其实现方法,着重介绍了DSPs芯片结构特点、运算速度、应用与市场,并展望了DSPs芯片的发展前景。