Neural network-based GPS GDOP approximation and classification

In this paper, the neural network (NN)-based navigation satellite subset selection is presented. The approach is based on approximation or classification of the satellite geometry dilution of precision (GDOP) factors utilizing the NN approach. Without matrix inversion required, the NN-based approach is capable of evaluating all subsets of satellites and hence reduces the computational burden. This would enable the use of a high-integrity navigation solution without the delay required for many matrix inversions. For overcoming the problem of slow learning in the BPNN, three other NNs that feature very fast learning speed, including the optimal interpolative (OI) Net, probabilistic neural network (PNN) and general regression neural network (GRNN), are employed. The network performance and computational expense on NN-based GDOP approximation and classification are explored. All the networks are able to provide sufficiently good accuracy, given enough time (for BPNN) or enough training data (for the other three networks).     

卷积神经网络GPS坐标转换方法

GPS坐标转换方法对于GPS空间定位系统至关重要。目前已有很多方法被提出用于转换GPS坐标,但效果并不是很显著。究其原因,是因为大多数都存在模型误差和投影误差。针对目前方法的不足,本文利用深度学习对非结构化数据处理的优势,提出了一种基于卷积神经网络(CNN)的GPS坐标转换方法。该方法将GPS数据转化为非结构化图片数据,以其作为CNN的输入层来训练GPS坐标转换模型,这样能够最小化满足对数据的预处理要求,无监督地从数据中学习出有效特征。试验结果表明,该方法与传统坐标转换方法相比,具有更高的转换精度。     

An intelligent and autonomous MEMS IMU/GPS integration scheme for low cost land navigation applications

An intelligent scheme to integrate inertial navigation system/global positioning system (GPS) is proposed using a constructive neural network (CNN) to overcome the limitations of current schemes, namely Kalman filtering (KF). The proposed CNN technique does not require prior knowledge or empirical trials to implement the proposed architecture since it is able to construct its architecture “on the fly,” based on the complexity of the vehicle dynamic variations. The proposed scheme is implemented and tested using Micro-electro-mechanical systems inertial measurement unit data collected in a land-vehicle environment. The performance of the proposed scheme is then compared with the multi-layer feed-forward neural networks (MFNN) and KF- based schemes in terms of positioning accuracy during GPS signal outages. The results are then analyzed and discussed in terms of positioning accuracy and learning time. The preliminary results presented in this article indicate that the positioning accuracy were improved by more than 55% when the MFNN and CNN-based schemes were implemented. In addition, the proposed CNN was able to construct the topology by itself autonomously on the fly and achieve similar prediction performance with less hidden neurons compared to MFNN-based schemes.     

关于BP神经网络转换GPS高程的若干问题

BP神经网络用于GPS高程转换有着较高的精度,但也存在不少问题,如网络的隐含层和隐含层单元个数选取、参加学习的样本的质量如何影响仿真精度等。结合实例分析上述问题,得出一些结论。     

A data archive of GPS navigation messages

Since 18 June 2007 navigation data messages transmitted by the GPS constellation are recorded by five receivers within GeoForschungsZentrum’s global groundstation network. We describe the recording, processing, validation, analysis and archiving of the navigation data. During the 197 days between 18 June 2007 and 31 December 2007 a total of 125,723,666 subframes were collected. By taking into consideration that the same data set frequently is observed by two or more receivers concurrently, 65,153,955 unique subframes could be extracted from the observations. With an estimated 88,099,200 subframes transmitted by the constellation during this time period a data yield of about 74% was achieved. Simulation studies suggest that with two additional GPS receivers, which are scheduled for addition to the network in 2008, about 95% of the transmitted subframes will be retrieved. The message data archive is open to the scientific community for non-commercial purposes and may be accessed through GFZ’s Information System and Data Center ().

Modelling local GPS/levelling geoid undulations using artificial neural networks

The use of GPS for establishing height control in an area where levelling data are available can involve the so-called GPS/levelling technique. Modelling of the GPS/levelling geoid undulations has usually been carried out using polynomial surface fitting, least-squares collocation (LSC) and finite-element methods. Artificial neural networks (ANNs) have recently been used for many investigations, and proven to be effective in solving complex problems represented by noisy and missing data. In this study, a feed-forward ANN structure, learning the characteristics of the training data through the back-propagation algorithm, is employed to model the local GPS/levelling geoid surface. The GPS/levelling geoid undulations for Istanbul, Turkey, were estimated from GPS and precise levelling measurements obtained during a field study in the period 1998–99. The results are compared to those produced by two well-known conventional methods, namely polynomial fitting and LSC, in terms of root mean square error (RMSE) that ranged from 3.97 to 5.73 cm. The results show that ANNs can produce results that are comparable to polynomial fitting and LSC. The main advantage of the ANN-based surfaces seems to be the low deviations from the GPS/levelling data surface, which is particularly important for distorted levelling networks.     

基于神经网络的GPS周跳探测的新方法

为了用GPS获得高精度的定位结果,周跳必须在数据处理过程中被检测和修复。通过对周跳产生原因和特点的分析,提出神经网络方法,并与传统方法进行比较。用神经网络建立模型对周跳进行预测。通过对比预测值的不同来确定周跳的大小,从而实现周跳的修复。此外,还利用实测的相位数据,验证方法的可行性与有效性。     

GPS导航技术在伊拉克战争中的应用

论述了GPS导航定位技术在伊拉克战争中的作用，着重分析了GPS在精确制导武器中的应用形式和效果，并预测了美国在GPS应用方面未来将要进行的努力。     

GPS车载导航定位技术研究

介绍车载导航定位系统的组成,比较几种导航方法的优缺点,得出几种较为合理、精度较高的组合定位方法.GPS卫星信号所包含的定位信息有其特定的格式,通过编写程序将其中所需的定位信息提取出来,并利用七参数转换方法将GPS定位系统应用的WGS-84坐标系转换成国家大地坐标系.通过车载导航试验实例分析表明,利用最短距离投影法得到的电子地图精度较高.     

BP神经网络在GPS导航中的应用

Kalman滤波常用于GPS动态数据的处理,由于系统存在的不确定性和非先验性,导致滤波产生较大的估计误差,甚至发散。介绍了BP神经网络算法及其非线性逼近能力,并基于BP神经网络的非线性逼近性能设计了BP神经网络进行GPS导航的新算法。实测数据计算结果表明该算法能够真实地反映载体运动轨迹,其导航解具有良好的精度和可靠性。     

VC多线程串口通讯编程技术在GPS导航的应用介绍

主要阐述了在GPS导航中，采用多线程异步串口通讯方式从GPS接收机实时读取导航定位数据的方法和过程。     

GPS桥梁平面控制网的坐标转换

针对桥梁施工控制网建立的特点,详细介绍了利用工程椭球进行高斯投影的一种实用GPS坐标转换方法,并通过天兴洲大桥GPS控制网的实例,证明了这种转换方法的可靠性。     

自适应卡尔曼滤波在GPS/DR组合导航中的应用

针对在观测条件较差的情况下卡尔曼滤波的鲁棒性较差,本文设计了一种自适应卡尔曼滤波模型。通过实际车载GPS/DR组合导航试验,结果表明该模型在观测质量较差的情况下能够抑制较大的偏差,相对于标准卡尔曼滤波模型,其平面定位精度提高了近一倍,达到2～3m。因此,在观测环境较差的情况下建议采用渐消自适应卡尔曼滤波模式进行组合导航。     

地基GPS反演大气水汽含量实践

随着地基GPS技术日益完善以及GPS在气象学中应用的不断深化,地基GPS探测技术已经成为一种有效的大气水汽探测手段,总结了地基GPS反演大气水汽含量的原理和方法,并通过实例分析了其误差产生的原因,验证了GPS反演大气水汽含量的可行性。     

GPS/MEMS INS integrated system for navigation in urban areas

This paper evaluates the performance of a tightly coupled GPS/INS integrated system based on low cost MEMS IMUs in dense urban areas, and investigates two different methods to improve its performance. The first method used is to derive observations from two different constraint equations reflecting the behavior of a typical land vehicle. The first constraint equation is derived assuming that the vehicle does not slip and always remains in contact with the ground. If these assumptions are true the velocity of the vehicle in the plane perpendicular to the forward direction should be zero. The second constraint equation is derived from the fact that the height does not change much in a short time interval in a land vehicular environment. Thus, when a GPS outage occurs (partial/complete), the integrated system combines the INS and constraints-derived virtual measurements to keep the position and velocity errors bounded. This method is suitable for use in real-time applications. The second method is specifically suitable for a post-mission application and involves the use of Rauch-Tung-Striebel (RTS) smoother. The designed system performance is evaluated using two data sets collected in dense urban areas. The obtained results demonstrate the effectiveness of different algorithms considered, in controlling the INS error growth, and indicates the potential of MEMS IMUs for use in land vehicle navigation applications.     

基于SINS／GPS的无人机组合导航系统建模与仿真

根据无人机的飞行特点以及SINS(捷联惯导)和GPS(全球定位系统)的优缺点,利用轨迹发生器设计无人机的飞行轨迹,建立基于SINS/GPS的无人机组合导航系统,依据卡尔曼滤波的相关原理对系统进行数学建模和计算仿真,验证无人机SINS/GPS组合导航系统的可靠性。     

Precision real-time navigation of LEO satellites using global positioning system measurements

Continued advancements in remote sensing technology along with a trend towards highly autonomous spacecraft provide a strong motivation for accurate real-time navigation of satellites in low Earth orbit (LEO). Global Navigation Satellite System (GNSS) sensors nowadays enable a continuous tracking and provide low-noise radiometric measurements onboard a user spacecraft. Following the deactivation of Selective Availability a representative real-time positioning accuracy of 10 m is presently achieved by spaceborne global positioning system (GPS) receivers on LEO satellites. This accuracy can notably be improved by use of dynamic orbit determination techniques. Besides a filtering of measurement noise and other short-term errors, these techniques enable the processing of ambiguous measurements such as carrier phase or code-carrier combinations. In this paper a reference algorithm for real-time onboard orbit determination is described and tested with GPS measurements from various ongoing space missions covering an altitude range of 400–800 km. A trade-off between modeling effort and achievable accuracy is performed, which takes into account the limitations of available onboard processors and the restricted upload capabilities. Furthermore, the benefits of different measurements types and the available real-time ephemeris products are assessed. Using GPS broadcast ephemerides a real-time position accuracy of about 0.5 m (3D rms) is feasible with dual-frequency carrier phase measurements. Slightly inferior results (0.6–1 m) are achieved with single-frequency code-carrier combinations or dual-frequency code. For further performance improvements the use of more accurate real-time GPS ephemeris products is mandatory. By way of example, it is shown that the TDRSS Augmentation Service for Satellites (TASS) offers the potential for 0.1–0.2 m real-time navigation accuracies onboard LEO satellites.     

GPS导航解算中几种非线性Kalman滤波的理论分析与比较

GPS导航解算中常采用离散线性Kalman滤波模型.由于线性化忽略高次项,加之线性化受初始值精度的影响,导致线性化模型精度很难满足高动态用户需求.为此,分别讨论了扩展Kalman滤波和Bancroft算法以及利用观测信息迭代精化观测方程三种算法,并结合算例进行了比较与分析.     

90年代动态 GPS 遥感信息对地定位系统

从GPS的特点、发展趋势及应用分类,介绍了GPS的概况及应用趋势。动态GPS遥感信息对地定位系统是GPS技术在遥感方面的应用。文中充分分析了以往遥感信息对地定位存在的问题,预测了GPS在遥感信息对地定位方面应用前景,并提出了发展这一系统的试验研究总体框图。     

Unscented Kalman filter with nonlinear dynamic process modeling for GPS navigation

This paper preliminarily investigates the application of unscented Kalman filter (UKF) approach with nonlinear dynamic process modeling for Global positioning system (GPS) navigation processing. Many estimation problems, including the GPS navigation, are actually nonlinear. Although it has been common that additional fictitious process noise can be added to the system model, however, the more suitable cure for non convergence caused by unmodeled states is to correct the model. For the nonlinear estimation problem, alternatives for the classical model-based extended Kalman filter (EKF) can be employed. The UKF is a nonlinear distribution approximation method, which uses a finite number of sigma points to propagate the probability of state distribution through the nonlinear dynamics of system. The UKF exhibits superior performance when compared with EKF since the series approximations in the EKF algorithm can lead to poor representations of the nonlinear functions and probability distributions of interest. GPS navigation processing using the proposed approach will be conducted to validate the effectiveness of the proposed strategy. The performance of the UKF with nonlinear dynamic process model will be assessed and compared to those of conventional EKF.