基于GPS的目标飞行时间异地高精度测量技术

针对远程目标异地作用的特性以及异地时间测量对时间统一标准的严格要求,提出了一种基于GPS的高精度异地目标光信号时间测量系统.该系统将GPS授时与本地温补晶振时钟结合起来,利用GPS授时秒脉冲1 pps进行时间同步,由本地时钟结合GPS的标准时间测量出飞行目标启动和停止的精确时间及时间间隔;同时设计了高精度的授时方案,对GPS秒脉冲进行监控和补偿,有效提高了系统的可靠性.实验表明,该系统测量在400 s内所产生的累积测量误差小于300μs,等效精度达到7.5×10-7.     

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。     

地震数据采集无线同步技术研究

针对地震勘探中时间同步问题和其他数据采集领域的采集同步问题,设计并完善基于GPS授时技术的同步技术方案,利用GPS授时信号全方位、实时性、连续性和高精度的特点,以GPS的PPS信号为基准来校准本地时钟,采用单片机和无线通信技术实现数据采集同步。通过对无线遥测各道地震数据进行同步处理,解决了当地震勘探仪采集的道数增多时数据采集和存储的实时性问题,特别是解决了未来数字式检波器中独立同步和分时处理等关键问题。     

北斗高精度授时终端测试技术研究

针对北斗授时终端提出了基于模拟环境下的定时精度及守时精度性能测试方法,使用卫星导航信号模拟器、铷钟、计数器搭建了模拟环境下北斗授时终端测试系统,北斗授时终端通过解算卫星信号,解算出北斗时间后,输出秒脉冲1PPS信号,实现和北斗模拟时间同步。测试系统解算北斗授时终端和卫星导航信号模拟器输出的秒脉冲1PPS信号之间的时间间隔,获取终端定时精度,测试结果表明测试方法具有较好的一致性和可重复性,实现了纳秒级别的定时精度测试。     

GPS授时信号同步方法研究与应用

针对卫星定位系统的校频和授时应用中的秒脉冲同步问题,提出了一种基于时间间隔测量和延迟线技术的精密脉冲同步技术方案,采用微处理器、时间间隔测量芯片和硅延迟电路,设计了测量控制电路,实现了脉冲宽范围精密同步,同步范围10ms,脉冲同步精度达到100ps,具有同步范围宽、同步精度高的特点。在基于GPS的精密校频授时中应用,实现本地时钟与GPS时钟精密同步。     

卫星导航接收机时延测定技术研究

利用卫星导航接收机进行高精度授时同步是一种简单有效的授时同步手段。导航接收系统时延的精确测量是进行高精度授时的前提,其输出秒脉冲信号的时延更是影响远距离授时、时间同步的关键。围绕卫星导航接收系统时延的测定,通过分析卫星导航接收机的时延构成及特点,建立了卫星导航接收机的时延构成模型,制定了便于操作的卫星导航接收机时延测定的方法,搭建了一套完整的卫星导航接收机时延测定系统,并对系统进行了标定和实验验证。     

基于神经网络辅助观测的连续组合导航算法研究

针对因全球定位系统(GPS)信号失效导致捷联式组合导航系统SINS/GPS组合导航系统发散的问题,设计了一种基于神经网络辅助观测的智能组合导航算法.该方法在GPS信号有效时训练神经网络,当GPS失效后利用神经网络自主重构组合导航系统,将神经网络的输出信息作为观测量构建新的Kalman滤波器,以实现对捷联惯性导航系统误差的连续反馈校正,从而实现了高精度的连续导航.该方法得到了仿真验证,从仿真结果可以看出,在GPS短时失效的情况下,该方法有效抑制了姿态角、速度和位置的发散现象,提高了组合导航系统的精度和可靠性.     

数字波形匹配的导航信号载波精密同步方法

导航信号质量评估中,为了避免中频采样下变频处理带来的非线性失真,采用微波直接采样信号.但是由于数据量庞大,传统的载波同步方法实现困难.针对这个问题,提出一种采用匹配滤波技术的载波精密同步方法.首先,用大步进量产生一系列初相不同的伪随机码调制本地波形,伪随机码与导航信号相同,对采样的导航信号进行匹配滤波,根据匹配滤波输出结果确定导航信号载波相位的粗估计值,进行载波粗剥离.然后进行导航信号的伪随机码同步,以载波相位粗估计值为中心,在一定范围内以更精细的步进量进行载波波形匹配,实现载波精密同步.最后对算法进行仿真,仿真结果表明提出算法实际可行,载波同步精度高,载波频率为L波段时,选择合适步长,同步误差可小于0.01弧度.     

多点定位系统时钟同步机制的应用

多点定位系统基于TDOA技术,在远端接收站获得目标的TOA信息,用本地时钟为该信号打上时标信息,送到中心站处理,由中心站计算接收站间的TDOA（到达时间差）,用得到的信息定位和识别目标。该文详细介绍多点定位系统不同的时钟同步技术,并对不同时钟同步技术的实现方式和优缺点进行分析,当大面积GPS干扰时,对多点定位系统出现系统失效的问题进行分析,并提出双模授时解决方案,对解决该类问题具有实际参考价值。     

基于XML的卫星导航电文格式建模

为了满足全球卫星导航多系统互操作的需要,本文提出了基于XML的导航电文格式建模方法.在分析各卫星导航系统电文结构的基础上,给出了导航电文结构的文档对象模型(DOM),为电文格式描述建立了统一的数据表达模型.根据文档对象模型,可以编写基于XML的导航电文描述文档,有效地为卫星导航多系统互操作提供了支持.     

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.     

一种新97小波消噪法在GPS/INS组合导航中的应用

针对GPS/INS组合导航中广泛采用的扩展卡尔曼滤波进行数据融合难以建立精确的数学模型问题,提出采用平移不变量的97提升小波算法对观测信号进行降噪.平移不变量可有效地消除信号在不连续点产生的伪吉布斯现象,97提升算法比传统的Mallat小波更具准确性和快速性.与传统的软、硬阈值函数相比,改进的阈值函数对组合导航信号处理具有更好的连续性.研究结果表明,改进的97提升平移小波消噪方法是一种节约计算时间、提高导航性能的优越方法.     

地方时间标准建立方案的研究

为满足卫星导航产品的检测需求,提出由国家时间基准驾驭的地方时间标准的建立方案,建立GPS共视法时间传递系统。通过数据比对使本地原子钟溯源到中国计量院的时间基准UTC(NIM),对比对数据用Vondrak平滑法进行滤波,并剔除异常数据。研究了影响比对精度的因素,包括:双频实测电离层时延,天线坐标设为固定模式,仰角选择30°以上计算,进一步提高了比对精度。作出预估方程,实现了实时的量值传递。实验结果表明,生成的地方时间标准与国家时间基准同步在±10 ns以内,并且提高了时标的准确性和稳定性。     

Optimizing of ANFIS for estimating INS error during GPS outages

Global positioning system (GPS) has been extensively used for land vehicle navigation systems. However, GPS is incapable of providing permanent and reliable navigation solutions in the presence of signal evaporation or blockage. On the other hand, navigation systems, in particular, inertial navigation systems (INSs), have become important components in different military and civil applications due to the recent advent of micro-electro-mechanical systems (MEMS). Both INS and GPS systems are often paired together to provide a reliable navigation solution by integrating the long-term GPS accuracy with the short-term INS accuracy. This article presents an alternative method to integrate GPS and INS systems and provide a robust navigation solution. This alternative approach to Kalman filtering (KF) utilizes artificial intelligence based on adaptive neuro-fuzzy inference system (ANFIS) to fuse data from both systems and estimate position and velocity errors. The KF is usually criticized for working only under predefined models and for its observability problem of hidden state variables, sensor error models, immunity to noise, sensor dependency, and linearization dependency. The training and updating of ANFIS parameters is one of the main problems. Therefore, the challenges encountered implementing an ANFIS module in real time have been overcome using particle swarm optimization (PSO) to optimize the ANFIS learning parameters since PSO involves less complexity and has fast convergence. The proposed alternative method uses GPS with INS data and PSO to update the intelligent PANFIS navigator using GPS/INS error as a fitness function to be minimized. Three methods of optimization have been tested and compared to estimate the INS error. Finally, the performance of the proposed alternative method has been examined using real field test data of MEMS grade INS integrated with GPS for different GPS outage periods. The results obtained outperform KF, particularly during long GPS signal blockage.     

Time transfer using multi-channel GPS receivers

This report is on time transfer experiments using a Global Positioning System (GPS) receiver constructed using a commercial GPS "engine" and a standard PC. The receiver measures the time difference between the local clock and a 1 pps signal synchronized to GPS time using data from up to 8 satellites. The receiver also reports the difference between GPS time as estimated using each of the satellites being tracked and the composite output pulses that have a rate of 1 Hz (1 pps signal). These data can be used to construct the standard 13-minute tracks as defined in the BIPM standard; the same data also can be averaged in other ways that make better use of the multi-channel capabilities of the hardware. The 13-minute averages can be directly compared with standard time-transfer receivers using common-view analysis. The results of the tests suggest that the methods currently used for national and international time and frequency coordination should be re-examined, and an alternative approach based on multi-channel receivers is suggested that should be more flexible, simpler, and easier to operate than the current system.     

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.     

The performance comparison between GPs and BeiDou‐2/compass: A perspective from Asia

Abstract

The next decade promises drastic improvements to global navigation satellite systems. The USA is modernizing GPS, Russia is refreshing GLONASS, Europe is moving ahead with its own Galileo system, and The People's Republic of China is expanding its BeiDou‐1 system from a regional navigation system to a full constellation global navigation satellite system known as BeiDou‐2/Compass, which consists of thirty five satellites including geostationary satellites, MEO satellites and geosynchronous satellites in the coming year. Extra satellites will make possible improved performance for all applications, and especially where satellite signals can be obscured, such as in urban canyons, under tree canopies or in open‐pit mines. The benefits of the expected extra satellites and their signals can be evaluated in terms of availability, accuracy, continuity, and reliability issues.

The advent of a hybrid GNSS constellation has drawn a lot of attention to study compatibility and interoperability. A number of performance analyses have been conducted on a global scale with respect to availability, reliability, accuracy and integrity in different simulated scenarios (such as open sky and urban canyons) for each system individually as well as for combined systems with all the possible combinations. Since the BeiDou‐2/Compass has gained more attention from GNSS communities, the main objective of this paper is to study the performance of BeiDou‐2/Compass comparied to GPS in the greater Asia region; and also to explore whether the combination of BeiDou‐2/Compass with GPS would yield performance improvements in this region.

The performance analysis can be analyzed by either the signal or the geometrical conditions. However, the scope of this study is limited to investigating the impact of current and future GNSS based on geometrical conditions. Therefore, the satellite visibility and DOP (Dilution of Precision) values of each system or possible combinations between them are used as the major indices for performance evaluation with the emphasis on the addition of Compass. In addition, those indices are further analyzed in terms of their spatial and temporal distributions with the emphasis on the greater Asia region. Moreover, the spatial performance analyses are conducted on both global and regional scales to provide more insightful comparisons to illustrate the importance of future Compass for users in the greater Asia region.     

Variational Bayesian Based IMM Robust GPS Navigation Filter

This paper investigates the navigational performance of Global Positioning System (GPS) using the variational Bayesian (VB) based robust filter with interacting multiple model (IMM) adaptation as the navigation processor. The performance of the state estimation for GPS navigation processing using the family of Kalman filter (KF) may be degraded due to the fact that in practical situations the statistics of measurement noise might change. In the proposed algorithm, the adaptivity is achieved by estimating the time-varying noise covariance matrices based on VB learning using the probabilistic approach, where in each update step, both the system state and time-varying measurement noise were recognized as random variables to be estimated. The estimation is iterated recursively at each time to approximate the real joint posterior distribution of state using the VB learning. One of the two major classical adaptive Kalman filter (AKF) approaches that have been proposed for tuning the noise covariance matrices is the multiple model adaptive estimate (MMAE). The IMM algorithm uses two or more filters to process in parallel, where each filter corresponds to a different dynamic or measurement model. The robust Huber's M-estimation-based extended Kalman filter (HEKF) algorithm integrates both merits of the Huber M-estimation methodology and EKF. The robustness is enhanced by modifying the filter update based on Huber's M-estimation method in the filtering framework. The proposed algorithm, referred to as the interactive multi-model based variational Bayesian HEKF (IMM-VBHEKF), provides an effective way for effectively handling the errors with time-varying and outlying property of non-Gaussian interference errors, such as the multipath effect. Illustrative examples are given to demonstrate the navigation performance enhancement in terms of adaptivity and robustness at the expense of acceptable additional execution time.