基于北斗与InSAR技术的城市水库群坝体变形监测体系研究

随着我国核心城市规模的不断扩大,城市水库群的安全问题变得非常重要。本文提出基于北斗与InSAR技术构建城市水库群坝体变形监测体系的概念,建立以InSAR卫星数据为核心的高效监测能力,并建设覆盖区域的北斗变形监测基准网,通过获取InSAR动态监测数据以及北斗卫星实时监测数据,辅助以人工监测数据,形成城市水库群坝体安全监测新模式。     

面向地表形变高精度监测的GNSS-InSAR融合方法北大核心CSCD

GNSS-InSAR数据融合进行监测地表形变是目前地表形变监测领域研究的热点问题,传统GNSS-InSAR数据融合方法融合简单、不能动态地反映地表形变特点,导致数据使用不充分、形变特征精度低等后果。提出了一种新的基于InSAR校正值和卡尔曼滤波的GNSS-InSAR融合方法。根据时间序列的GNSS观测值和InSAR校正观测值的时空相关性,通过卡尔曼滤波对两种数据进行融合,得到更精确的地表三维形变结果。利用2018年11月15日至2022年6月3日103景Sentinel-1A数据和同期13个GNSS点位数据进行处理,实验结果表明:校正后的InSAR观测值与GNSS观测值经卡尔曼滤波融合结果比未校正的InSAR观测值与GNSS观测值融合结果精度高45%,比InSAR观测值精度高57%。因此,基于InSAR校正值和卡尔曼滤波的GNSS-InSAR融合模型提高了InSAR变形监测的精度,拓展提升InSAR应用范围的广度和深度。     

GNSS技术与多传感器融合的滑坡监测系统研究

本文设计了一种基于GNSS技术与多传感器融合的滑坡监测系统,该系统采用低功耗设计思路,通过RT-Thread操作系统实现多任务处理与低功耗模式运行,具有与滑坡监测预警云平台通信、GNSS网络RTK实时动态高精度定位、多传感器融合、监测数据实时采集处理、滑坡地质灾害预警等功能。实验结果表明,该系统运行功耗低、数据传输速度快、可靠性高、位移监测精度可达毫米级,满足对滑坡体进行高精度变形监测预警的需求。     

基于GPS 的尾矿库坝体位移在线监测系统设计

尾矿库作为一种高势能人造泥石流危险源,其坝体位移的在线监测对防止溃坝灾害的发生起到至关重要的作用,利用GPS技术精度高、抗干扰性强等特点并采用实时差分变形监测技术来测量坝体表面关键点的水平和竖直位移,利用RS232和GPRS无线传输技术来实现数据的采集和传输,并加装可靠的防雷和电源供电装置,可以大大提高矿山安全的生产系数。     

GNSS掩星探测软件设计与实现*

GNSS掩星探测具有全天候观测、高垂直分辨率和全球覆盖等优势。大气环境的复杂性要求掩星接收机既要实时快速,也要参数自适应。基于软件接收机和掩星理论,设计了一种应用于掩星探测的软件接收机,以C/C 为编程语言,实现了掩星信号捕获、跟踪、开环处理、提取载波相位、特征多普勒二维重现等算法。软件接收机可以灵活改变开环跟踪算法的参数,可重配性和可扩展性强,可兼容GPS和北斗系统。软件充分考虑了掩星数据的失锁处理和结果的可视化。实测数据验证了软件的有效性。     

区域MODIS水汽季节修正模型

针对MODIS水汽空间分辨率高但精度不高的不足,提出了一种利用GNSS的MODIS水汽校正模型。MODIS水汽校正模型通过GNSS水汽和线性回归方法分季节构建。通过GNSS水汽与MODIS水汽的相关性分析比较,利用线性回归方法分季节构建区域和城市MODIS水汽校正模型,通过与GNSS水汽比较验证区域和城市模型的可靠性。研究结果表明,GNSS水汽与MODIS水汽的变化趋势基本一致,存在显著正相关特性;经检验3个测站的城市和区域,冬季模型均方根误差优于1 mm,春、秋季均方根误差接近,约为2 mm,夏季城市模型均方根误差最大,值为6.58 mm。区域模型有效提高MODIS水汽精度,可为短时天气预报提供基础。     

基于时间序列TerraSAR-X影像的线状地物沉降监测试验研究

新一代高分辨雷达卫星尤其是TerraSAR-X的发射运行,给时序InSAR分析技术带来新的挑战,得益于其高分辨率特点使得特定目标的短期内微小形变监测成为可能。本文利用2009年3月～2010年1月间27景3m分辨率的TerraSAR-X数据,基于改进的时间序列InSAR技术获得京津城际高铁武清段和北辰段沿线区域地表时序形变特征,并对形变规律及成因进行分析。     

融合5G/GNSS的车辆高精度鲁棒安全定位:进展与展望

不受环境和条件影响的准确、实时定位对于基于位置的车辆应用和自动驾驶至关重要.典型车辆定位通常依赖于全球卫星导航系统(GNSS),如美国GPS、中国北斗等,由于易受遮挡和阻塞,常将其与惯导、视觉等技术融合弥补GNSS缺陷.但车规级传感器易受驾驶状态、天气等因素影响,很难精确测量,影响定位性能.近年来,依托先进5G技术和广域基础设施建设,5G/GNSS融合定位可以提供更为精确鲁棒实时的位置结果,并逐渐成为车辆高精定位的主要手段.鉴于极少有车辆定位领域应用5G/GNSS融合方法的系统综述,面向车辆定位,从精度、鲁棒、实时安全等多方面分述基于5G/GNSS融合的先进定位方法,并探讨研究空白和未来研究方向.     

城市内涝三维可视化应急管理信息系统

为提升城市防洪减灾能力,通过融合北斗卫星监控、大数据分析、云服务和综合集成等技术,设计城市内涝三维可视化应急管理信息系统。构建包含感知层、数据层、服务层、应用层和客户层的系统体系架构,采用北斗技术实现多源信息的全天候、多维度动态监测与传输,同时基于Hadoop平台对海量数据资源进行分析和存储,构建城市三维可视化集成环境,利用综合集成技术为用户提供个性化的内涝应急管理服务。以西安市为研究区域,实现系统多源信息资源分析、内涝监测、内涝预警和内涝预案等功能模块。应用结果表明,该系统可提供全天候、多方位的内涝应急管理服务,适用性强、可扩展性好,能够为科学应对城市内涝提供决策支持。     

基于北斗的无人机空气质量监测系统设计

为了监测城市发展带来的有害物质如PM1.0,PM2.5,PM10,SO2,NO2等,设计了一种基于北斗定位的无人机空气质量监测系统。该设计利用C语言实现硬件的数据传输功能;Java语言和数据库相结合,实现部分网页功能;利用传感器技术获取相关信息,借助网络通信技术实现网页与硬件部分通信。该系统对于提高环境监测的实时性、安全性等具有深远意义。     

Super-resolution imaging based on the BeiDou B3 signal

ABSTRACT

Using global navigation satellites to construct bi-static synthetic aperture radar for imaging has been a major research hotspot in passive radar. However, the low range resolution of Global Navigation Satellite signal (GNSS) limits the quality of actual scene imaging. To increase the range resolution of the imaging, a super-resolution imaging method by mixing the back-projection (BP) algorithm with truncated singular value decomposition (TSVD) is proposed. This paper first introduces the BeiDou Navigation Satellite System (BDS) signal model for ground imaging, carries out the range compression and describes the BP algorithm. Subsequently, the super-resolution method is given and some simulation results are demonstrated. Two field experimental cases, including targets of trees and ferries, are then carried out. The experimental results demonstrate the effectiveness of the proposed method.     

基于GNSS/GIS/RS集成技术的铁路列车运营监控

文章阐述了全球导航卫星系统GNSS(GlobalNavigationSatelliteSystem)、地理信息系统GIS(GeographicInfor-mationSystem)、遥感RS(RemoteSensing)技术的基本特征、相互关系及其集成基本方式。针对我国目前智能铁路运输系统发展趋势,运用GNSS/GIS/RS集成技术,进行铁路列车运营监控探讨性研究。该集成导航技术将卫星导航、地理信息系统以及遥感技术融合在一起,可实现铁路运输管理“自动化”、列车行驶“智能化”,减少运输服务时间,提高铁路运输整体效益,并且具有良好的实用价值。     

新的改进K均值粒子群算法在组合导航的应用

在捷联惯导/卫星导航（SINS/GNSS）紧组合导航系统的非线性非高斯高动态模型中,一般K均值粒子群优化（PSO）算法易出现粒子退化、滤波发散等问题。针对上述问题,提出一种融入权值修正的K均值粒子群滤波方法。通过观测SINS/GNSS紧组合导航系统的精度因子(GDOP),来修正粒子权值,从而修正每个K均值的聚类中心的权重,进而优化粒子;并结合SINS/GNSS紧组合导航系统模型进行了仿真分析。结果表明在非线性非高斯高动态的情况下,该改进算法有效地抑制了滤波发散,提高了精度。     

A Novel Vehicle-Based GNSS Integrity Augmentation System for Autonomous Airport Surface Operations

Autonomous vehicles equipped with integrity augmentation systems offer the potential to increase safety, efficiency and sustainability of airport ground operations. The model predictive behavior of these systems supports a timely detection of any deviations from the Required Navigation Performance (RNP), producing useful alerts for onboard mission management. Firstly, the system architecture of a Navigation and Guidance System (NGS) for autonomous airport surface vehicle operations based on Global Navigation Satellite System (GNSS) measurements is described. Subsequently, an integrity augmentation module is implemented in the NGS by modeling the key GNSS signal degradation phenomena including masking, multipath and signal attenuation. The GNSS integrity augmentation system is capable of monitoring the RNP and alerting the remote operator of the airport surface vehicle. The uniqueness of the presented system is that both caution and warning flags are produced based on prediction-avoidance and reaction-correction capabilities respectively. Additionally, the system is capable of issuing suitable steering commands to the onboard mission management system/remote ground base station operator in the event of GNSS signal degradations or losses. Multipath is modelled in detail using a ray tracing algorithm and the vehicle position error is computed as a function of relative geometry between the satellites, receiver antenna and reflectors in realistic airport operation scenarios. Additionally, the surface vehicle dynamics and reflective surfaces of buildings are modelled in order to simulate a vehicle trajectory through a typical airport airside/aprons environment. Simulation case studies are performed to validate the mathematical models developed for the integrity augmentation system and the results corroborate the suitability of the proposed system to generate useful and timely integrity flags when GNSS is used as the primary means of navigation.     

A data fusion system of GNSS data and on-vehicle sensors data for improving car positioning precision in urban environments

Accurate car positioning on the Earth's surface is a requirement for many state-of-the-art automotive applications, but current low-cost Global Navigation Satellite System (GNSS) receivers can suffer from poor precision and transient unavailability in urban areas. In this article, a real-time data fusion system of absolute and relative positioning data is proposed with the aim of increasing car positioning precision. To achieve this goal, a system based on the Extended Kalman Filter (EKF) was employed to fuse absolute positioning data coming from a low-cost GNSS receiver with data coming from four wheel speed sensors, a lateral acceleration sensor, and a steering wheel angle sensor. The bicycle kinematic model and the Ackerman steering geometry were employed to particularize the EKF. The proposed system was evaluated through experimental tests. The results showed precision improvements of up to 50% in terms of the Root Mean Square Error (RMSE), 50% in terms of the 95th-percentile of the distance error distribution, and 75% in terms of the maximum distance error, with respect to using a stand-alone, low-cost GNSS receiver. These results suggest that the proposed data fusion system for car vehicles can significantly reduce the positioning error with respect to the positioning error of a low-cost GNSS receiver. The best precision improvements of the system are expected to be achieved in urban areas, where tall buildings hinder the effectiveness of GNSS systems. The main contribution of this work is the proposal of a novel system that enables accurate car positioning during short GNSS signal outages. This advance could be integrated in larger expert and intelligent systems such as autonomous cars, helping to make self-driving easier and safer.     

卫星信号模拟器CBOC中频信号设计与实现

GNSS卫星信号模拟器对于GNSS系统及用户接收机的研究和设计起着非常重要的作用。本文主要研究针对Galileo系统E1频点CBOC中频信号的产生方法及数字处理技术,在基于PCIE+DSP+FPGA+DAC架构的中频板卡上完成了与PC上位机通信、波形控制参数计算和更新、副载波生成、基带信号调制以及模拟中频信号的产生;最后给出了频谱、相关峰、定位误差等测量结果,验证了信号的质量和正确性。     

Cooperative surveillance systems and digital-technology enabler for a real-time standard terminal arrival schedule displacement

As the backbone of Communications, Navigation and Surveillance Systems for Air Traffic Management (CNS/ATM), Automatic Dependent Surveillance-Broadcast (ADS-B) is a surveillance technology and digital-technology enabler relying on the Global Navigation Satellite System (GNSS). The onboard ADS-B Out system broadcasts the aircraft’s real-time digital information such as position and ground speed periodically (every 0.5–2 s), which is more frequent than the radar system. Taking this advantage, situational awareness and flight efficiency can be highly improved. In this paper, a novel heuristic search method based on ADS-B is proposed for the Aircraft Landing Problem (ALP) with the objective of reducing flight time while maintaining the time separation standards mandated by the International Civil Aviation Organization (ICAO). The recorded ADS-B data in Shanghai Hongqiao and Pudong international airports are adopted to demonstrate the performance of the proposed method. Results show that there is an obvious decrease in the total flight time. Besides, the heuristic search method can achieve continuous and real-time ALP updates, satisfying the requirements for air traffic control. While highlighting ADS-B-based applications, this study also provides some basic implications for the updated model in air traffic management.     

GNSS-R遥感国内外研究进展

全球导航卫星系统(Global Navigation Satellite System,GNSS)除传统的导航和定位功能外,其反射信号(Global Navigation Satellite Signal Reflectometry,GNSS-R)也可以被接收和利用,成为一种新型的遥感手段。本文对GNSS-R技术在海洋遥感和陆面遥感方面的研究进展进行评述,并从GNSS-R接收机、反演模型和星载GNSS-R试验3个方面阐述其关键技术,在此基础上提出GNSS-R研究中存在的问题及发展方向。     

基于Elman神经网络的GNSS/INS全域高精度定位方法

针对当前智能网联汽车定位与导航系统无法接收全球导航卫星系统（GNSS）信号引起定位失效的问题，提出一种基于Elman神经网络的GNSS结合惯性导航系统（INS）的全域高精度定位方法。首先，采用神经网络方法，建立了基于Elman网络的GNSS/INS高精度定位训练模型和GNSS失效预测模型；然后，利用GNSS、INS和实时动态（RTK）等定位技术，设计了GNSS/INS高精度定位数据采集实验系统；最后，选取采集的有效实验数据进行了反向传播（BP）神经网络、级联BP（CFBP）神经网络、Elman神经网络的训练模型性能对比分析，并验证了基于Elman网络的GNSS失效预测模型。实验结果表明，所提方法训练误差指标均优于基于BP和CFBP神经网络的方法；在GNSS失效1 min、2 min、5 min时，基于预测模型的预测平均绝对误差（MAE）、方差（VAR）和均方根误差（RMSE）分别为18.88 cm、19.29 cm、58.83 cm，8.96、8.45、5.68和20.90、21.06、59.10，随着GNSS信号失效时长的增加，定位预测精度降低。