基于DSP和FPGA的多通道GPS中频信号源设计与实现

卫星导航信号源是卫星导航接收机研制过程中的重要测试设备。采用软件无线电技术,实现了一种基于DSP+FPGA的多通道GPS中频信号源。基于GPS中频信号数学模型,给出了系统整体设计方案。采用模块化设计,详细阐述了DSP与FPGA的数据交互逻辑、总线接口模块、载波NCO模块、码模块和导航电文缓冲模块等系统关键部分的实现及重要参数设置的依据,并给出Model Sim-Altera 6.4a下各模块的仿真结果。实测结果表明,信号源生成中频信号波形、频谱符合GPS信号体制规范,且能够被软件接收机正确捕获、跟踪。该信号源具有良好的实时性和可扩展性,成本可控,可有效应用于实验室环境下接收机的性能测试。     

Rotator attitude measurement using GPS based on Kalman filter algorithm

研究了用单天线全球定位系统(GPS)接收机的载波信号正弦相位调制波实时测量旋转体姿态的基本原理和方法,设计了一种实现旋转体姿态测量的卡尔曼滤波算法.为了克服多天线姿态测量存在的天线相位延迟、噪声相互独立等不利因素,将单天线置于相位中心偏离旋转轴心位置.分析了旋转体姿态与GPS接收机载波信号之间的关系,给出载波信号的数学模型,在GPS接收机跟踪环路建立了包含姿态信息的载波信号卡尔曼滤波模型,对环路的跟踪性能与姿态算法进行MATLAB仿真.结果表明,此算法的跟踪环路带宽窄,能有效抑制噪声干扰,较好地实现了对旋转信号的跟踪,并且姿态测量精度比较高.     

GPS在时间频率测量中的应用简介

介绍了怎样将GPS(全球定位系统)的卫星信号应用在时间和频率计量中,讨论了GPS接收机如何实现时间同步和频率校准,阐明了GPS在时间和频率测量中应用的几种类型.     

GPS卫星可见性预测与接收机的快速定位

为了解决GPS接收机快速捕星问题,提高接收机定位速度,利用历书信息进行GPS卫星预报,提出了一种GPS卫星可见性预测的改进算法.在星历信息基础之上,用此算法对卫星的可见性及概略多普勒频移进行预测,并在GPS信号模拟器和GPS接收机组建的实验平台上,利用此种方法进行捕星定位实验.实验结果显示:该方法定位时间为原定位时间的39.5%.     

基于逐级干扰对消的微弱GPS信号捕获算法

为实现微弱GPS信号处理中接收到的信号存在强卫星信号干扰的情况下对微弱GPS信号的捕获,提出了一种基于逐级干扰对消的捕获算法,即依照卫星信号载噪比,由大到小逐级进行捕获,前一级的输入信号对消掉该级捕获的最强信号作为下一级的输入,再进行下一级信号的捕获,且捕获时由卫星信号载噪比自适应调整所需的累加时间,以期在最短时间内实现信号的成功捕获.采用逐级干扰对消捕获算法,可最大限度地减小强信号对微弱信号捕获的影响.理论分析及仿真结果表明,该方法具有更强的信号捕获能力.     

一种新型GPS冗余授时方法

大量采用GPS授时的网络存在安全隐患,而采用其它授时方法则难以在现有设备增加外部时钟接口.介绍了基于GPS射频信号模拟的外部授时方法,对原有设备无需作任何改动,从外部授时模块中获得外部同步的1 pps(pulses per second)和时间信息,仿真产生固定点接收到GPS信号的导航电文和观测数据,并将GPS导航电文的时间信息实时修改;GPS射频信号基于仿真数据产生,使设备内嵌的GPS接收机正常定位授时,恢复出外部同步的1 pps,从而实现外部授时.论文介绍了采用大规模FPGA的实现方法,实验结果验证了对于固定位置该授时方法的有效性.     

关于GPS在测绘工作中应用的调查报告

1GPS简介1．1GPS构成GPS主要由空间卫星星座、地面监控站及用户设备三部分构成。（1）GPS空间卫星星座由21颗工作卫星和3颗在轨备用卫星组成。24颗卫星均匀分布在6个轨道平面内，轨道平面的倾角为55°，卫星的平均高度为2072007km，运行周期为11h58min。卫星用L玻段的两个无线电载波向广大用户连续不断地发送导航定位信号，导航定位信号中含有卫星的位置信息，     

SINS/GPS组合导航的扩展Kalman滤波算法

针对捷联惯性导航系统(SINS)无法长时间单独工作和GPS卫星信号易失锁而无法定位的问题,分析了两种导航系统的优缺点,提出了SINS/GPS组合导航的方法.建立了陀螺和加速度计的误差模型,采用松耦合方式,设计了扩展Kalman滤波器.以姿态、速度、位置的误差以及陀螺、加速度计的误差作为状态变量,对姿态、速度、位置进行校正.运用Matlab对组合导航系统进行了仿真.结果表明,该算法简单,容易实现,能满足导航精度要求.     

一种GPS C/A码信号产生器

对GPS卫星信号的组成进行讨论,提出一种GPSC/A码信号产生器设计结构,使用Modelsim软件工具对C/A码信号产生器进行功能仿真。仿真证明所设计C/A码信号产生器的正确性。     

HY-2卫星双频GPS精密定轨技术

海洋二号(HY-2)卫星双频全球定位跟踪系统(GPS)接收机获取的GPS数据是基于非差简化动力学方法应用于HY-2卫星精密轨道确定。差分GPS载波相位数据可以消除钟差,同时利用GPS数据获得HY-2卫星轨道和GPS卫星轨道。GPS数据确定的轨道利用激光测距进行了检核,并与法国太空总署确定的轨道以及综合定轨策略确定的轨道进行了比较,HY-2卫星基于GPS数据精密轨道径向成分优于3cm。     

移动式机动车现场标准测速仪溯源技术的研究

介绍了当前广泛使用的两种基于光电和GPS原理的非接触移动式机动车现场标准测速仪的溯源技术,对光电式机动车速度仪和高精度机动车GPS测速仪进行了实验室模拟测速误差的检测和机动车道路速度实测值的对比试验。在10～180km/h范围内特定速度点,采用同步齿形带式台架校准装置对光电式机动车速度仪进行校准;采用机动车GPS信号模拟器对高精度机动车GPS测速仪在相同速度点进行模拟测速误差检测。使用机动车道路速度实测方法对两类测速仪器在20～150km/h速度范围内的测速性能进行对比,结果表明,两类测速仪器在速度波动较小时,不同速度点的测速偏差在±0.3%范围内;速度变化较快时,高精度机动车GPS测速仪的速度测量值响应速度较快,而光电式机动车速度仪的速度响应与之相比有一定的延时滞后,两者的最大测速偏差在±8%范围内。因此,在能够搜到6颗以上卫星信号时,使用高精度机动车GPS测速仪具有更好的测速性能。     

复杂结构回声信号模拟器的设计

介绍了一种利用DDS技术设计的复杂结构信号模拟器,它可以模拟多种地层回声或复杂结构的信号,同时重点介绍了DDS技术,并以结构复杂的地层回声信号为例介绍了信号模拟器的基本原理及构成,本文所述的复杂信号模拟器的设计方法具有普遍意义。     

Support Vector Machine Assisted GPS Navigation in Limited Satellite Visibility

This paper investigates performance improvement via the incorporation of the support vector machine (SVM) into the vector tracking loop (VTL) for the Global Positioning System (GPS) in limited satellite visibility. Unlike the traditional scalar tracking loop (STL), the tracking and navigation modules in the VTL are not independent anymore since the user’s position can be determined by using the information from other satellites and can be predicted on the basis of the states of the user. The method proposed in this paper makes use of the SVM to bridge the GPS signal and prevent the error growth due to signal outage. Similar to the neural network, the SVM is motivated by its ability to approximate an unknown nonlinear input-output mapping through supervised training. The SVM is employed for predicting adequate numerical control oscillator (NCO) inputs, i.e., providing better prediction of residuals for the Doppler frequency and code phase in order to maintain regular operation of the navigation system. When the navigation processing is in good condition, the SVM is at the training stage, and the output information from the discriminator and navigation filter is adopted as the inputs. Other machine learning (ML) algorithms such as the radial basis function neural network (RBFNN) and the Adaptive Network-Based Fuzzy Inference System (ANFIS) are employed for comparison. Performance evaluation for the SVM assisted architecture as compared to the RBFNN- and ANFIS-assisted methods and the un-assisted VTL will be carried out and the performance evaluation during GPS signal outage will be presented. The proposed design is very useful for navigation during the environment of limited satellite visibility to effectively overcome the problem in the environment of GPS outage.     

Kernel Entropy Based Extended Kalman Filter for GPS Navigation Processing

This paper investigates the kernel entropy based extended Kalman filter (EKF) as the navigation processor for the Global Navigation Satellite Systems (GNSS), such as the Global Positioning System (GPS). The algorithm is effective for dealing with non-Gaussian errors or heavy-tailed (or impulsive) interference errors, such as the multipath. The kernel minimum error entropy (MEE) and maximum correntropy criterion (MCC) based filtering for satellite navigation system is involved for dealing with non-Gaussian errors or heavy-tailed interference errors or outliers of the GPS. The standard EKF method is derived based on minimization of mean square error (MSE) and is optimal only under Gaussian assumption in case the system models are precisely established. The GPS navigation algorithm based on kernel entropy related principles, including the MEE criterion and the MCC will be performed, which is utilized not only for the time-varying adaptation but the outlier type of interference errors. The kernel entropy based design is a new approach using information from higher-order signal statistics. In information theoretic learning (ITL), the entropy principle based measure uses information from higher-order signal statistics and captures more statistical information as compared to MSE. To improve the performance under non-Gaussian environments, the proposed filter which adopts the MEE/MCC as the optimization criterion instead of using the minimum mean square error (MMSE) is utilized for mitigation of the heavy-tailed type of multipath errors. Performance assessment will be carried out to show the effectiveness of the proposed approach for positioning improvement in GPS navigation processing.     

伪随机码超声扩频测距系统设计与算法

针对单脉冲回波方式的超声测距方法存在的缺陷,借鉴雷达中的脉冲压缩技术,介绍了基于伪随机二进制序列(m序列)的超声扩频测距实现方法.伪随机码与超声载波采用ASK(幅移键控)调制方式,介绍了伪随机码超声扩频测距硬件方案和超声测距时延估计模型.针对超声换能器带宽特性和单脉冲回波特点,分析了m序列参数设计方法.提出了一种基于FFT的伪随机码包络相关快速时延估计算法,该算法将信号解调与匹配相关融合,大大降低了运算量.实验表明,本文介绍的伪码超声扩频测距硬件方案和快速算法可在便携式超声测距系统中实现,并可在多基站超声定位系统中推广使用.     

Complementary Kalman Filter as a Baseline Vector Estimator for GPS-Based Attitude Determination

The Global Positioning System (GPS) offers the interferometer for attitude determination by processing the carrier phase observables. By using carrier phase observables, the relative positioning is obtained in centimeter level. GPS interferometry has been firstly used in precise static relative positioning, and thereafter in kinematic positioning. The carrier phase differential GPS based on interferometer principles can solve for the antenna baseline vector, defined as the vector between the antenna designated master and one of the slave antennas, connected to a rigid body. Determining the unknown baseline vectors between the antennas sits at the heart of GPS-based attitude determination. The conventional solution of the baseline vectors based on least-squares approach is inherently noisy, which results in the noisy attitude solutions. In this article, the complementary Kalman filter (CKF) is employed for solving the baseline vector in the attitude determination mechanism to improve the performance, where the receiversatellite double differenced observable was utilized as the measurement. By using the carrier phase observables, the relative positioning is obtained in centimeter level. Employing the CKF provides several advantages, such as accuracy improvement, reliability enhancement, and real-time assurance. Simulation results based on the conventional method where the least-squares approach is involved, and the proposed method where the CKF is involved are compared and discussed.     

The Utilization of Artificial Neural Networks for Multisensor System Integration in Navigation and Positioning Instruments

Inertial-navigation system (INS) and global position system (GPS) technologies have been widely applied in many positioning and navigation applications. INS determines the position and the attitude of a moving vehicle in real time by processing the measurements of three-axis gyroscopes and three-axis accelerometers mounted along three mutually orthogonal directions. GPS, on the other hand, provides the position and the velocity through the processing of the code and the carrier signals of at least four satellites. Each system has its own unique characteristics and limitations. Therefore, the integration of the two systems offers several advantages and overcomes each of their drawbacks. The integration of INS and GPS is usually implemented utilizing the Kalman filter, which represents one of the best solutions for INS/GPS integration. However, the Kalman filter performs adequately only under certain predefined dynamic models. Alternatively, this paper suggests an INS/GPS integration method based on artificial neural networks (ANN) to fuse uncompensated INS measurements and differential GPS (DGPS) measurements. The proposed method suggests two different architectures: the position update architecture (PUA) and the position and velocity PUA (PVUA). Both architectures were developed utilizing multilayer feed-forward neural networks with a conjugate gradient training algorithm.     

窄带干扰下GPS接收机捕获性能的研究

针对GPS接收机抗干扰能力强的特点．分析了窄带干扰对接收机的捕获性能的影响．在扩频理论的基础上．给出了GPSC／A码接收机信号捕获的数学模型，计算得出平均捕获时间与捕获时间方差，给出窄带干扰下的接收机信号捕获的仿真结果．结果表明．当窄带干扰功率超过一定的值时，会导致GPS接收机捕获性能的严重恶化．     

A Methodology for the Evaluation of a GPS Receiver Performance in Telematics Applications

This paper presents a methodology to evaluate the position availability of automotive grade global positioning system (GPS) receivers intended for Telematics applications utilizing a multichannel GPS satellite signal simulator in a controlled laboratory environment. Initially, field testing of two distinct GPS receivers was conducted in an urban canyon environment and a foliage environment to assess each receiver's position availability performance. Test scenarios were then developed on a multichannel GPS satellite signal simulator in order to create controlled and repeatable stimuli to the GPS receivers. The scenarios take into account the actual satellite constellations at the same day, time, and locations of the field data collections. Furthermore, the number of visible satellites and power levels was adjusted in order to stimulate the hardware tracking sensitivity, hardware acquisition sensitivity, dynamic range, and navigation filter design, all of which impact position availability for GPS receivers. Quantitative results demonstrated good correlation between the results obtained using the developed test scenarios and the results from the field testing. The proposed methodology will result in reducing validation cost and time to market for automotive Telematics products