Performance Assessment of GNSS Measurements from Android Platform

被引:0
|
作者
Liu, Jiyuan [1 ]
Hu, Yifan [1 ]
Zhang, Dongsong [2 ,3 ]
Liu, Huafu [2 ,3 ]
机构
[1] Natl Univ Def Technol, Sch Comp Sci, Changsha, Hunan, Peoples R China
[2] Watercraft Coll, Zhenjiang, Jiangsu, Peoples R China
[3] Changsha Univ, Dept Math & Comp Sci, Changsha, Hunan, Peoples R China
来源
PROCEEDINGS OF 2017 6TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND NETWORK TECHNOLOGY (ICCSNT 2017) | 2017年
基金
中国国家自然科学基金;
关键词
Android positioning; GNSS measurements assessment;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Positioning on mobile phones performs a fundamental role in our daily life. Recently, Google released some new interfaces to obtain GNSS measurements which provide application developers a new option to calculate position on Android phones. In this research, the measurements are collected in Google Nexus 9 operating on Android 7.0 OS. First, some aspects, such as VSN (Visible Satellite Number), HDOP (Horizontal Dilution of Precision) and CNR (Carrier to Noise Ratio), are assessed. Then, estimated position and velocity are calculated and assessed. Finally, the precision of position generated from integrating the velocity information into estimated position with KF (Kalman Filter) is also evaluated. We believe our assessments will give application developers guidance when they use Android GNSS measurements in positioning.
引用
收藏
页码:472 / 476
页数:5
相关论文
共 50 条
  • [21] Precise Positioning with Machine Learning based Kalman Filter using GNSS/IMU Measurements from Android Smartphone
    Han, Kahee
    Lee, Subin
    Song, Young-Jin
    Lee, Hak-Beom
    Park, Dong-Hyuk
    Won, Jong-Hoon
    PROCEEDINGS OF THE 34TH INTERNATIONAL TECHNICAL MEETING OF THE SATELLITE DIVISION OF THE INSTITUTE OF NAVIGATION (ION GNSS+ 2021), 2021, : 3094 - 3102
  • [22] PACE: Platform for Android Malware Classification and Performance hvaluation
    Kumar, Ajit
    Agarwal, Vinti
    Shandilya, Shishir K.
    Shalaginov, Andrii
    Upadhyay, Saket
    Yadav, Bhawna
    2019 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2019, : 4280 - 4288
  • [23] Performance Analysis of Symmetric Block Cryptosystems on Android Platform
    Grgic, Kresimir
    Kovacevic, Zoran
    Cik, Visnja Krizanovic
    PROCEEDINGS OF 2017 INTERNATIONAL CONFERENCE ON SMART SYSTEMS AND TECHNOLOGIES (SST), 2017, : 155 - 159
  • [24] The Performance Assessment on Lunar Navigation for GNSS
    Qu, Bo
    Yan, Tao
    Han, Xingyuan
    Wang, Yanguang
    Li, Longlong
    Meng, Yansong
    CHINA SATELLITE NAVIGATION CONFERENCE (CSNC) 2018 PROCEEDINGS, VOL III, 2018, 499 : 387 - 397
  • [25] Raw GNSS observations from Android smartphones: characteristics and short-baseline RTK positioning performance
    Gao, Rui
    Xu, Li
    Zhang, Baocheng
    Liu, Teng
    MEASUREMENT SCIENCE AND TECHNOLOGY, 2021, 32 (08)
  • [26] GNSS Observation Generation from Smartphone Android Location API: Performance of Existing Apps, Issues and Improvement
    Zangenehnejad, Farzaneh
    Jiang, Yang
    Gao, Yang
    SENSORS, 2023, 23 (02)
  • [27] The Whole Works: A GNSS/IMU Tight Coupled Filter for Android Raw GNSS Measurements with Local Ground Augmentation Strategies
    Fortunato, Marco
    Tagliaferro, Giulio
    Fernandez-Rodriguez, Eva
    Critchley-Marrows, Joshua
    PROCEEDINGS OF THE 34TH INTERNATIONAL TECHNICAL MEETING OF THE SATELLITE DIVISION OF THE INSTITUTE OF NAVIGATION (ION GNSS+ 2021), 2021, : 3103 - 3126
  • [28] AGC on Android Devices for GNSS
    Lee, Dong-Kyeong
    Spens, Nicholas
    Gattis, Benon
    Akos, Dennis
    PROCEEDINGS OF THE 2021 INTERNATIONAL TECHNICAL MEETING OF THE INSTITUTE OF NAVIGATION, 2021, : 33 - 41
  • [29] Assessment of Commercial GNSS Radio Occultation Performance from PlanetiQ Mission
    Zhran, Mohamed
    Mousa, Ashraf
    Wang, Yu
    Ben Hasher, Fahdah Falah
    Jin, Shuanggen
    REMOTE SENSING, 2024, 16 (17)
  • [30] PrNet: A Neural Network for Correcting Pseudoranges to Improve Positioning With Android Raw GNSS Measurements
    Weng, Xu
    Ling, K. V.
    Liu, Haochen
    IEEE INTERNET OF THINGS JOURNAL, 2024, 11 (14): : 24973 - 24983