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Proceedings of the 2023 International Technical Meeting of The Institute of Navigation最新文献

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Adaptive Robust-Statistics GNSS Navigation Based on Environmental Context Detection 基于环境上下文检测的自适应鲁棒统计GNSS导航
Yi Ding, Florent Feriol, Yoko Watanabe, Philippe Asseman, Gael Pages, Damien Vivet
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引用次数: 0
GNSS Signal Authentication using QZSS Signal and Evaluation of Key Performance Indicators 基于QZSS信号的GNSS信号认证与关键性能指标评估
D. Manandhar, R. Shibasaki
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引用次数: 0
Experimental Results of Integrity Monitoring for UAV Flights in Urban Environment Leveraging Image Based Masking 基于图像掩蔽的城市环境下无人机飞行完整性监测实验结果
H. Engwerda, Merle Snijders, Juliette Casals Sadlier
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引用次数: 0
Protection Level Formulations for Distance Measuring Equipment (DME) 距离测量设备(DME)防护等级规定
Brandon Weaver, Gianluca Zampieri, O. Osechas
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引用次数: 1
Performance Analysis of a Cellular LTE and GPS L1 C/A Vector Tracking Receiver, a Simulation Study 蜂窝LTE和GPS L1 C/ a矢量跟踪接收机的性能分析,仿真研究
Samuel C. Morgan, Scott M. Martin
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引用次数: 1
The use of the Frequency Difference of Arrival of 1090 MHz ADS-B Signals for Wide Area Multilateration 1090mhz ADS-B信号到达频率差在广域复用中的应用
Shang-Ping Weng, Shau-Shiun Jan
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引用次数: 0
Timekeeping with a Chip Scale Atomic Clock in GPS Denied Environments 在GPS拒绝环境下用芯片级原子钟计时
Jade Babcock-Chi, Lucca Trapani, D. Akos
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引用次数: 0
Assessment of Differential Doppler Navigation with Starlink LEO Satellite Signals of Opportunity 利用“机遇号”星链低轨道卫星信号对差分多普勒导航进行评估
S. Hayek, Joe Saroufim, M. Neinavaie, Sharbel E. Kozhaya, Z. M. Kassas
A framework for differential Doppler navigation with Starlink low Earth orbit (LEO) space vehicle (SV) signals of opportunity is presented, and the framework’s performance is assessed. The differential framework assumes a rover (vehicle) navigating without global navigation satellite system (GNSS) signals, through the simultaneous tracking and navigation (STAN) approach. In STAN, the vehicle aids its inertial navigation system (INS) with Doppler measurements extracted from LEO SVs via an extended Kalman filter (EKF), simultaneously estimating the vehicle’s and LEO SVs’ states. In addition, the differential framework assumes a stationary base with a known position, making Doppler measurements to the same Starlink LEO SVs and communicating these measurements with the rover. The objective of the proposed differential framework is to mitigate the effects of poorly known LEO SVs’ ephemerides, unknown LEO SVs’ dynamic clock error states, and atmospheric delays. Simulation results are presented to assess the performance of the proposed differential framework compared to a non-differential STAN. The simulations assume an aerial vehicle equipped with a tactical-grade inertial measurement unit (IMU) and an altimeter, navigating for 28 km in 300 seconds, the last 23 km of which without GNSS, while receiving signals from 14 Starlink LEO SVs. It is shown that the non-differential STAN achieves a position root-mean squared error (RMSE) of 15.63 m, while the differential STAN with one, two, and three bases reduces the position RMSE to 5.26 m, 3.88 m, and 1.94 m, respectively. Experimental results are presented in which a stationary base and a stationary rover, located at a distance of 1 km apart, extract Doppler observables from 3 Starlink LEO SVs. The differential framework was able to estimate the rover’s three-dimensional (3-D) and 2-D position with an error of 33.4 m and 5.6 m, respectively
提出了一种基于星链低地球轨道飞行器(SV)机会信号的差分多普勒导航框架,并对该框架的性能进行了评估。差分框架假设漫游车(车辆)通过同步跟踪和导航(STAN)方法在没有全球导航卫星系统(GNSS)信号的情况下进行导航。在STAN中,车辆通过扩展卡尔曼滤波器(EKF)从LEO SVs中提取多普勒测量值来辅助其惯性导航系统(INS),同时估计车辆和LEO SVs的状态。此外,差分框架假设一个已知位置的固定基地,对相同的Starlink LEO sv进行多普勒测量,并将这些测量结果与漫游车进行通信。提出的差分框架的目的是减轻鲜为人知的LEO sv星历、未知的LEO sv动态时钟误差状态和大气延迟的影响。给出了仿真结果来评估所提出的差分框架与非差分STAN的性能。模拟假设一架配备战术级惯性测量单元(IMU)和高度计的飞行器,在300秒内导航28公里,其中最后23公里没有GNSS,同时接收来自14个Starlink LEO SVs的信号。结果表明,非差分STAN的位置均方根误差(RMSE)为15.63 m,而带有一个、两个和三个碱基的差分STAN的位置均方根误差分别为5.26 m、3.88 m和1.94 m。本文介绍了静止基地和静止漫游车在距离1 km处提取3颗Starlink LEO sv多普勒观测数据的实验结果。差分框架能够估计漫游车的三维(3-D)和二维位置,误差分别为33.4米和5.6米
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引用次数: 0
Solution Separation-Based Integrity Monitoring for Integer Ambiguity Resolution-Enabled GNSS Positioning 基于解分离的整数模糊度GNSS定位完整性监测
Shizhuang Wang, X. Zhan, Y. Zhai, Zhen Gao
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引用次数: 2
On the Performance of a Cascade of Barriers of Multipath Feared Events for High Accuracy High Integrity Train Positioning 高精度、高完整性列车定位中多路径恐惧事件障碍级联性能研究
Michele Brizzi, Sandro Chiocchio, A. Persia, A. Neri
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引用次数: 1
期刊
Proceedings of the 2023 International Technical Meeting of The Institute of Navigation
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