GNSS卫星信号模拟器码和载波NCO研究与实现

NCO是卫星信号模拟器中频信号处理的关键部分;介绍了GNSS卫星信号模拟器码NCO和载波NCO的原理及作用,建立DDS模型,确定基本参数并根据参数设计了码NCO和载波NCO基本结构;给出了码NCO和载波NCO的实现过程,利用verilog在Xilinx'ISE 11.2和modelsim6.5中完成载波NCO和码NCO的设计和仿真,在FPGA中进行了实现,并给出仿真波形和信号频谱图;本码NCO和载波NCO模块已应用于某型GNSS模拟器样机,通过程序仿真与样机测试,证明本码NCO和载波NCO模块性能满足GNSS卫星信号模拟器系统需求.     

QPSK高性能数字调制器的FPGA实现

本文提出了一种QPSK高性能数字调制器的FPGA实现方案,由Altera的IP核NCO2.3.0提供QPSK高稳定度的数字正弦信号,给出了QPSK的实验仿真结果。结果表明,基于NCO的QPSK数字调制器极大地改善了无杂散动态范围及信噪比,有效地降低了FPGA的硬件开销,QPSK调制器工作稳定、可靠,达到设计要求。     

一种基于FPGA的数控振荡器设计与实现

数值控制震荡器（NCO）是一种通信领域广泛使用的装置。文章首先介绍了数值控制震荡器的工作原理，然后重点介绍一种基于现场可编程门阵列（FPGA）的NCO的电路设计。采用Verilog语言描述硬件电路，在QuartusⅡ平台上进行设计和仿真，并给出了电路的仿真波形，电路易于用FPGA器件实现，具有良好的应用前景。     

无人机测控系统中数控振荡器的FPGA实现与仿真

无人机的测控系统要求数控振荡器(NCO)的频率分辨率高、相位噪声低、杂散小.在分析相位截断、幅度量化会导致NCO的输出产生杂散的基础上,提出了抖动信号注入与函数值校正的混合算法,并利用FPGA芯片实现了一种适用的频率、相位、抖动信号、校正因子等参数可调的NCO,取得了较好的整体性能.仿真结果显示:设计的NCO适合无人机的测控系统,并与理论分析相吻合.     

数控振荡器应用及其在FPGA中的实现

本文首先描述NCO的基本工作原理,然后介绍利用NCO产生调频信号(FM)、频移键控信号(FSK)、相移键控信号(PSK)、调幅信号(AM)和幅度键控信号(ASK)等多种调制信号的方法,最后以调幅信号(AM)为例介绍调制信号在FPGA中的实现。     

WCDMA直放站数字下变频的FPGA实现

数字下变频器（Digital Down Converter,DDC）是WCDMA直放站的重要组成部分,它将高速采样的数字中频信号下变频到基带,然后进行抽取,低通滤波。重点研究了数字下变频器的数控振荡器NCO和半带滤波器的原理和硬件设计仿真,通过FPGA芯片Virtex-6 XC6VLX75T设计实现了适用于WCDMA直放站的数字下变频器,并对其进行硬件仿真与验证。     

基于FPGA与Matlab的数字正交解调器的设计

为有效提取测控系统输入信号的幅度和相位信息,设计了基于FPGA与Matlab的信号数字正交解调器;在Matlab/Simulink 环境中产生一路调幅信号,并在此环境下利用5个直接I型的4阶FIR滤波器节搭建了20阶FIR滤波器;利用FPGA查表法实现数控振荡器(NCO),并控制1路调幅信号与正交的正、余弦信号分别进行数字混频处理;对经FPGA数字混频处理后的两路倍频分量和基频分量信号进行滤波处理,经处理后的信号在FPGA的控制下进行相加处理;最后在硬件平台上进行了仿真测试实验,验证了该方案的正确性和可行性.     

非相干扩频测控系统距离模拟电路设计

针对非相干体制扩频测控系统信号模拟器的要求,提出的基于现场可编程门阵列(FPGA)与DSP的信号模拟器距离模拟电路设计及实现方案.该距离模拟电路采用计数器与伪码数控振荡器(NCO)共同实现对伪码扩频信号的距离精确模拟,经过硬件电路实测结果表明,该距离模拟电路模拟精度高,满足系统的要求.     

信道化接收机的结构优化和实现

为了减少信道化接收机的资源消耗,对低通滤波器组实现信道化接收机的结构进行了研究。在前人将HB滤波器和FIR滤波器设计为多通道并采用时分复用方法的基础上,将NCO和CIC滤波器也做了同样处理,并在FPGA上分别实现了优化前后的两种结构,通过硬件资源消耗情况的对比,验证了此方法的有效性。在输入数据为单一频率正弦波的情况下,将信道化的结果导入Matlab进行分析,验证了此方法的正确性。     

一种高效灵活数字上变频FPGA设计

数字上变频器是软件无线电的核心部件之一,其基本功能是增加基带信号采样率并把其搬移到载波频率上.本文采用内插滤波器特性较好的积分梳妆滤波CIC和补偿滤波器CFIR级联的插值滤波器结构,载频可编程的数控振荡器(NCO)在Ahera FPGA EP2SGX90上实现了稳定可靠的数字上变频器.     

基于查找表和XC2V1000的高精度教控振荡器实现

根据数控振荡器的工作原理，提出用FPGA芯片XC2V1000与静态随机存储器联合实现高精度数控振荡器的方法，并给出采用此结构设计的数控振荡器的特点和性能。     

基于FPGA的正交数字混频器的设计与验证

本文研究了用DDS加乘法器实现正交数字混频器的设计及其完整的验证方法,用DDS产生的正/余弦正交本振序列与模拟信号通过A/D采样数字化后的数字序列相乘,再通过数字低通滤波实现数字混频。其中DDS采用正弦和余弦波形幅值存储功能依靠片内EAB实现,省去了片外ROM,符合片上系统(SoC)的思想;用MATLAB软件增强QUARTUS的仿真功能,得到的仿真结果完整而且直观。     

基于CORDIC算法的NCO在FPGA中的实现

介绍如何利用CORDIC（Coordination Rotation Digital Computer）算法产生正余弦信号的实现过程基础上,研究并在FPGA中实现了基于流水线CORDIC算法的数控振荡器。仿真验证结果表明,该方法较之其它方法具有精度高、结构简单易于实现、节省资源且功耗低等特点,非常适合应用于高速高精度数字调制解调。     

Analysis of self-describing gridded geoscience data with netCDF Operators (NCO)

The netCDF Operator (NCO) software facilitates manipulation and analysis of gridded geoscience data stored in the self-describing netCDF format. NCO is optimized to efficiently analyze large multi-dimensional data sets spanning many files. Researchers and data centers often use NCO to analyze and serve observed and modeled geoscience data including satellite observations and weather, air quality, and climate forecasts. NCO's functionality includes shared memory threading, a message-passing interface, network transparency, and an interpreted language parser. NCO treats data files as a high level data type whose contents may be simultaneously manipulated by a single command. Institutions and data portals often use NCO for middleware to hyperslab and aggregate data set requests, while scientific researchers use NCO to perform three general functions: arithmetic operations, data permutation and compression, and metadata editing. We describe NCO's design philosophy and primary features, illustrate techniques to solve common geoscience and environmental data analysis problems, and suggest ways to design gridded data sets that can ease their subsequent analysis.     

NCO tracking and self-optimizing control in the context of real-time optimization

This paper reviews the role of self-optimizing control (SOC) and necessary conditions of optimality tracking (NCO tracking) as presented by [1]. We show that self-optimizing control is not an alternative to NCO tracking for steady state optimization, but is to be seen as complementary. In self-optimizing control, offline calculations are used to determine controlled variables (CVs), which by use of a lower layer feedback controller indirectly keep the process close to the optimum when a disturbance enters the process. Preferably, the setpoints are kept constant, but they may be adjusted by some optimization layer. Good CVs reduce the need for frequent setpoint changes. When selecting self-optimizing CVs, a set of disturbances must be assumed, as unexpected disturbances are not rejected in SOC. On the other hand, the presented NCO tracking procedure adapts the inputs at given sample times without a model or any assumptions on the set of disturbances. However, disturbances with high frequencies or those which do not lead to a steady state are not rejected. By using NCO tracking in the optimization layer and SOC in the lower control layer, we demonstrate that the methods complement each other, with SOC giving fast optimal correction for expected disturbances, while other disturbances are compensated by the model free NCO tracking procedure on a slower time scale.     

Use of measurements for enforcing the necessary conditions of optimality in the presence of constraints and uncertainty

Measurements can be used in an optimization framework to compensate the effects of uncertainty in the form of model mismatch or process disturbances. Among the various options for input adaption, a promising approach consists of directly enforcing the necessary conditions of optimality (NCO) that include two parts, the active constraints and the sensitivities. In this paper, the variations of the NCO due to parametric uncertainty are studied and used to design appropriate adaptation laws. The inputs are separated into constraint-seeking and sensitivity-seeking directions depending on which part of the NCO they enforce. In addition, the directional influence of uncertainty is used to reduce the number of variables to adapt. The theoretical concepts are illustrated in simulation via the run-to-run optimization of a batch emulsion polymerization reactor.