基于场景检测的城市环境GNSS/INS组合定位方法研究

城市复杂环境下,采用GNSS/INS组合定位方法能有效提升GNSS信号部分或全部被遮挡情况下的车辆连续定位能力。GNSS信号受遮挡的程度具有随机性和突变性,给GNSS/INS组合算法的架构设计和参数设置带来了挑战。基于此,设计了一套基于场景检测的GNSS/INS组合定位策略与方法,提出了以多星GNSS观测量的八类特征为输入,利用支持向量机分类思想将定位场景分为室外开阔、室外遮挡和室内三种类型,并建立了与之相适应的组合滤波量测误差估计模型,进而实现了GNSS/INS组合定位。实测数据验证表明,提出的方法能够有效提升车辆组合定位精度。     

Android智能手机双频GNSS伪距差分动态定位性能分析

随着智能手机全球导航卫星系统(GNSS)天线和芯片从单频单模向多频多模快速发展,基于其所衍生出来的位置服务(LBS)应用极大地便利了大众用户的日常生活.然而受限于低成本、低功耗的信号接收与处理单元,手机在无增强信息的情况下仅依赖伪距单点定位难以为用户提供稳定、高精度的导航服务.因此,基于小米8手机(Mi8)的GNSS双频原始数据,采用非组合的双频伪距观测值、载波历元差分观测值和多普勒观测值构建了滤波定位模型,并引入伪距差分数据,以提升手机定位的连续性和精度.在较复杂环境下开展了行人和车载实验,实测结果表明:双频定位精度与单频相比提升了15％～30％,伪距差分定位精度和单点定位相比提升了5％～20％,行人和车载双频伪距差分定位的平面位置误差分别为0.65m和1.03m,基本满足手机用户在城市复杂环境下的高精度定位服务需求.     

An optimized strategy for inter-satellite links assignments in GNSS

Inter-Satellite Links (ISLs) can effectively enhance the Global Navigation Satellite System (GNSS) performance, such as communication and ranging performance. These performances would have been improved by disposing sparsely of ground stations and ISLs. Here, we demonstrate a topology scheduling strategy based on Signed Variance and Blossom algorithm (SV-Blossom) to promote the topology performance of GNSS. We also analyze the effect of this strategy on the robustness of the ranging information quality of the ISLs assignments system. For the domestic and overseas satellite communication Time Delay (TD) and Position Dilution of Precision (PDOP), by analyzing the relation of satellites, a set of the ISLs cost models is established, and the cost matrix is obtained. The final ISLs assignments result is obtained by processing the link cost matrix with the Blossom algorithm in the graph theory. In order to verify the rationality of the SV-Blossom strategy, a typical BDS-3 simulation scene with 10080 min is built to test these performances of ISLs. The final results show that the ranging information quality of the SV-Blossom strategy is greatly improved compared with recently published strategies, and the proposed links assignments mechanism has good robustness.     

BeiDou-3 orbit and clock quality of the IGS Multi-GNSS Pilot Project

Within the Multi-GNSS Pilot Project (MGEX) of the International GNSS Service (IGS), precise orbit and clock products for the BeiDou-3 global navigation satellite system (BDS-3) are routinely generated by a total of five analysis centers. The processing standards and specific properties of the individual products are reviewed and the BDS-3 orbit and clock product performance is assessed through direct inter-comparison, satellite laser ranging (SLR) residuals, clock stability analysis, and precise point positioning solutions. The orbit consistency evaluated by the signal-in-space range error is on the level of 4–8 cm for the medium Earth orbit satellites whereas SLR residuals have RMS values between 3 and 9 cm. The clock analysis reveals sytematic effects related to the elevation of the Sun above the orbital plane for all ACs pointing to deficiencies in solar radiation pressure modeling. Nevertheless, precise point positioning with the BDS-3 MGEX orbit and clock products results in 3D RMS values between 7 and 8 mm.     

A real-time combined quality control method for GNSS precise positioning in harsh environments

Global Navigation Satellite System (GNSS) precise positioning can be significantly affected by severe multipath effects and outliers in harsh environments, and highly relies on quality control strategies. Previous studies mainly focus on the posterior residuals to check and exclude the outliers in GNSS observations, limited work emphasizes the combined quality control method considering both the prior and posterior knowledge simultaneously. This paper proposed a real-time combined quality control method to process the multipath effects and outliers in harsh environments simultaneously. Specifically, in the prior stage, a modified multipath processing strategy is proposed for both phase and code observations, then a modified detection, identification, and adaptation (DIA) method considering the maximum times of data snooping is studied in the posterior stage. Two dedicated experiments in real harsh environments were carried out to evaluate the performance of the proposed combined quality control method. For the static experiment, the proposed method exhibits smaller positioning errors, the best positioning accuracy, and the highest availability in this study. Specifically, the proposed method exhibits an improved percentage of 55.4 %, 56.3 %, and 59.7 % for positioning accuracy compared to those without the quality control method in the E, N, and U directions, respectively. Besides, the proposed method can further improve the performance of ambiguity resolution with an improved percentage of 32.2 %. For the kinematic experiment, the three-dimensional positioning accuracy of the proposed method is 0.577 m, which exhibits a 40.0 % improvement compared to those without the quality control method. Also, the proposed method exhibits better performance under relatively strong multipath effects. In this sense, the proposed real-time combined quality control method is highly appreciated in terms of positioning availability, accuracy, and ambiguity resolution for GNSS precise positioning, especially in harsh environments.     

An improved cyclic multi model-eXtreme gradient boosting (CMM-XGBoost) forecasting algorithm on the GNSS vertical time series

The study of GNSS vertical coordinate time series forecasting is helpful for monitoring the crustal plate movement, dam or bridge deformation monitoring, and global or regional coordinate system maintenance. The eXtreme Gradient Boosting (XGBoost) algorithm is a machine learning algorithm that can evaluate features, and it has a great potential and stability for long-span time series forecasting. This study proposes a multi-model combined forecasting method based on the XGBoost algorithm. The method constitutes a new time series as features through the fitting and forecasting results of the forecasting model. The XGBoost model is then used for forecasting. In addition, this method can obtain higher precision forecasting results through circulation. To verify the performance of the forecasting method, 1095 epochs of data in the Up coordinate of 16 GNSS stations are selected for the forecasting test. Compared with the CNN-LSTM model, the experimental results of our forecasting method show that the mean absolute error (MAE) values are reduced by 30.23 %～52.50 % and the root mean square error (RMSE) values are reduced by 31.92 %～54.33 %. The forecasting results have higher accuracy and are highly correlated to the original time series, which can better forecast the vertical movement of the GNSS stations. Therefore, the forecasting method can be applied to the up component of the GNSS coordinate time series.     

基于基线长度加权的GNSS多天线姿态测量方法

GNSS可用于获取动态载体的姿态信息，为提高其姿态测量精度，提出一种基于基线长度信息对最小二乘姿态解算方法进行加权处理，改进了最小二乘姿态解算方法，同时通过船载实验验证了该方法的可用性。实验结果表明：在船载动态实验中，当基线长度存在明显差异时，加权最小二乘法较等权最小二乘法在精度上明显提高，其中横滚角的精度提高了13 %。     

GNSS复杂电磁环境综合监测测向与分析评估技术

复杂的电磁环境是电磁辐射源集合与电波传播环境相互作用的总和.针对目前GNSS易受复杂电磁环境影响的问题,研究了GNSS复杂电磁环境综合监测测向与分析评估技术,提出了专用信道化监测方法、比幅与比相联合测向方法、卫星导航信号和电磁干扰信号综合分析方法以及GNSS复杂电磁环境影响效应分析评估方法,解决了现有通用设备在弱信号监测测向、电磁干扰信号与导航信号联合监测以及在复杂电磁环境影响效应分析评估方面存在的不足.该技术能够指导GNSS复杂电磁环境综合监测测向与分析评估设备的研制,为GNSS复杂电磁环境监测保障的工程化提供了技术支持.     

基于VBGMM-DCNN的列车卫星定位欺骗干扰检测方法

面向基于全球导航卫星系统的铁路列车定位实施欺骗干扰的主动检测，在卫星定位解算层次，运用深度学习建模学习方法的优势，提出一种基于变分贝叶斯高斯混合模型-深度卷积神经网络（variational Bayesian Gaussian mixture model-deep convolutional neural network, VBGMM-DCNN）的列车卫星定位欺骗干扰检测方法。该方法首先提取能够充分体现欺骗干扰对定位解算过程作用影响的卫星观测特征参数，构建干扰检测特征矢量；然后，采用VBGMM模型拟合经过预处理的特征向量的概率分布，得到二维概率密度图；最后，将概率密度图用于DCNN模型实施欺骗干扰的检测决策。结合现场实验所得运行场景数据，利用实验室搭建的欺骗干扰测试环境实施了干扰注入测试与检验，结果表明,欺骗干扰检测性能随着DCNN网络深度的增加而提升，相对于常规有监督决策方法F1值最高提升44.68%。基于VBGMM-DCNN的欺骗干扰检测能够适应测试验证中运用的列车运行特征及定位观测条件，所达到的检测性能优于对比算法。     

NeQuick2 validation using multi-satellite and ground data

Global Navigation Satellite System’s (GNSS) positioning calculation is prone to ionospheric errors. Single frequency GNSS users receive ionospheric corrections through broadcast ionospheric models. Therefore, the accuracy of ionospheric models must be validated based on various geographic and geomagnetic conditions. In this work, an attempt is made to validate NeQuick2 electron density (Ne) using multiple sources of space-based and ground-based data at the Arabian Peninsula and for low solar activity conditions. These sources include space-based data from Swarm, DMSP and COSMIC-2 satellite constellations and ground-based data from GNSS receiver and the ionosonde. The period of this study is 1 year from October 2019 to September 2020. Analysis shows that the agreement between NeQuick2 and experimental Ne close to the peak density height depends on seasons and time of the day with the largest errors found in Autumn and during the daytime. NeQuick2 generally overestimates Ne during the daytime. During the early morning and evening hours, Ne estimates seem to be fairly accurate with slight underestimation in Winter and Spring. Estimation of slab thickness by NeQuick2 is found to be close to the values calculated using collocated ionosonde and GNSS receiver.