基于极大似然估计器的GNSS矢量跟踪算法

矢量跟踪是一种将全球导航卫星系统(GNSS)接收机的信号跟踪与导航解算融为一体的跟踪算法。传统的基于矢量延迟/频率锁定环(VDFLL)的跟踪算法普遍采用延迟锁定环(DLL)和锁频环(FLL)鉴别器计算伪距和伪距率偏差观测量,由于锁频环鉴别器存在近似误差和一步延迟效应,在高动态环境下容易造成环路失锁。从直接估计卫星信号特征参数的角度出发,基于中频信号模型构建码相位和载波多普勒的极大似然代价函数,采用非迭代估计算法得到各通道码相位和多普勒频移的估计偏差,转换为卡尔曼滤波器的观测矢量,提出一种基于极大似然估计器(MLE)的矢量跟踪算法。理论分析和仿真结果表明:新算法结合了极大似然估计和矢量跟踪的优点,克服了FLL的延迟效应,与基于VDFLL的矢量环路相比,高动态环境下的跟踪稳定性更好,可以对被遮挡的卫星保持持续的跟踪。     

Stochastic modelling considering ionospheric scintillation effects on GNSS relative and point positioning

Global Navigation Satellite Systems (GNSS), in particular the Global Positioning System (GPS), have been widely used for high accuracy geodetic positioning. The Least Squares functional models related to the GNSS observables have been more extensively studied than the corresponding stochastic models, given that the development of the latter is significantly more complex. As a result, a simplified stochastic model is often used in GNSS positioning, which assumes that all the GNSS observables are statistically independent and of the same quality, i.e. a similar variance is assigned indiscriminately to all of the measurements. However, the definition of the stochastic model may be approached from a more detailed perspective, considering specific effects affecting each observable individually, as for example the effects of ionospheric scintillation. These effects relate to phase and amplitude fluctuations in the satellites signals that occur due to diffraction on electron density irregularities in the ionosphere and are particularly relevant at equatorial and high latitude regions, especially during periods of high solar activity. As a consequence, degraded measurement quality and poorer positioning accuracy may result.     

GNSS驯服芯片级原子钟方法研究

芯片级原子钟是一种体积小且功耗低的高精度时钟源,具有广泛的用途。针对这一特点,设计了基于GNSS的芯片级原子钟驾驭算法。以GNSS系统时作为参考,测量芯片级原子钟与GNSS系统时间的钟差,并对芯片级原子钟进行钟差建模,获取其特征参数。通过乒乓法计算出钟驾驭调整量,对芯片级原子钟进行控制,最终将芯片级原子钟驾驭到GNSS系统时间上。经过实验验证,在驾驭时间常数为100s的情况下,芯片级原子钟与GNSS系统时间的时钟同步误差在-7.5~7.5ns之间;1h频率准确度为5.8×10-13;平均时间为10000s时的频率稳定度为3×10-13。     

Instantaneous BeiDou–GPS attitude determination: A performance analysis

The advent of modernized and new global navigation satellite systems (GNSS) has enhanced the availability of satellite based positioning, navigation, and timing (PNT) solutions. Specifically, it increases redundancy and yields operational back-up or independence in case of failure or unavailability of one system. Among existing GNSS, the Chinese BeiDou system (BDS) is being developed and will consist of geostationary (GEO) satellites, inclined geosynchronous orbit (IGSO) satellites, and medium-Earth-orbit (MEO) satellites. In this contribution, a BeiDou–GPS robustness analysis is carried out for instantaneous, unaided attitude determination.     

一致性检验方法在导弹多惯组测量中的应用

传感器测量的准确与可靠是减小融合系统不确定性的前提,通过对多传感器一致性的检验,可以确定多传感器之间的测量误差,判断检测系统可靠性的程度。提出了应用概率距离测量对多传感器信息进行一致性检验的方法,并将此方法引入到基于多惯性测量组合的总线式导弹制导控制系统中。实例证明,该方法对于确保和提高测量导弹飞行信息的精度具有良好的效果。     

The ground GNSS tomography – unconstrained approach

The GNSS signal along its more than 20,000 km line of sight is bended, attenuated and delayed. These effects are results of the Earth’s atmosphere, and Sun radiation. Amongst all mentioned effects the one considered in this paper is a signal phase delay in troposphere.     

Using TensorFlow-based Neural Network to estimate GNSS single frequency ionospheric delay (IONONet)

In the last 20?years, and in particular in the last decade, the availability of propagation data for GNSS has increased substantially. In this sense, the ionosphere has been sounded with a large number of receivers that provide an enormous amount of ionospheric data. Moreover, the maturity of the models has also been increased in the same period of time. As an example, IGS has ionospheric maps from GNSS data back to 1998, which would allow for the correlation of these data with other quantities relevant for the user and space weather (such as Solar Flux and Kp). These large datasets would account for almost half a billion points to be analyzed. With the advent and explosion of Big Data algorithms to analyze large databases and find correlations with different kinds of data, and the availability of open source code libraries (for example, the TensorFlow libraries from Google that are used in this paper), the possibility of merging these two worlds has been widely opened. In this paper, a proof of concept for a single frequency correction algorithm based in GNSS GIM vTEC and Fully Connected Neural Networks is provided. Different Neural Network architectures have been tested, including shallow (one hidden layer) and deep (up to five hidden layers) Neural Network models. The error in training data of such models ranges from 50% to 1% depending on the architecture used. Moreover, it is shown that by adjusting a Neural Network with data from 2005 to 2009 but tested with data from 2016 to 2017, Neural Network models could be suitable for the forecast of vTEC for single frequency users. The results indicate that this kind of model can be used in combination with the Galileo Signal-in-Space (SiS) NeQuick G parameters. This combination provides a broadcast model with equivalent performances to NeQuick G and better than GPS ICA for the years 2016 and 2017, showing a 3D position Root Mean Squared (RMS) error of approximately 2?m.     

基于低频磁信标的全天时自主定位方法

由于以GPS、北斗为代表的GNSS信号容易受到天气和位置的影响,单纯使用GNSS在某些条件下会有较大的定位误差,甚至无法提供定位服务。针对这种情况研究了基于低频磁信标的自主定位方法,分析了电磁学中的毕奥-萨伐尔定律等原理,建立了基于低频磁信标定位的系统模型,给出了分离式双信标定位方法。通过实验分析了信标安装的误差特性,最后通过仿真和实际实验验证了该定位方法的有效性,对于室内机器人导航、地下生物探索、水下跟踪定位等单独采用GNSS定位影响较大的场景下的自主定位与导航具有重要的工程应用价值。