BDS-PPP-B2b-Based Smartphone Precise Positioning Model Enhanced by Mixed-Frequency Data and Hybrid Weight Function

Compared to high-cost hardware-based Global Navigation Satellite System (GNSS) positioning techniques, smartphone-based precise positioning technology plays an important role in applications such as the Internet of Things (IoT). Since Google released the Nougat version of Android in 2016, this has p...

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Main Authors: Zhouzheng Gao, Zhixiong Wu, Shiyu Liu, Cheng Yang
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Applied Sciences
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Online Access:https://www.mdpi.com/2076-3417/15/13/7169
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author Zhouzheng Gao
Zhixiong Wu
Shiyu Liu
Cheng Yang
author_facet Zhouzheng Gao
Zhixiong Wu
Shiyu Liu
Cheng Yang
author_sort Zhouzheng Gao
collection DOAJ
description Compared to high-cost hardware-based Global Navigation Satellite System (GNSS) positioning techniques, smartphone-based precise positioning technology plays an important role in applications such as the Internet of Things (IoT). Since Google released the Nougat version of Android in 2016, this has provided a new method for achieving high-accuracy positioning solutions with a smartphone. However, two factors are limiting smartphone-based high-accuracy applications, namely, real-time precise orbit/clock products without the internet and the quality-adaptive precise point positioning (PPP) model. To overcome these two factors, we introduce BDS PPP-B2b orbit/clock corrections and a hybrid weight function (based on C/N<sub>0</sub> and satellite elevation) into smartphone real-time PPP. To validate the performance of such a method, two sets of field tests were arranged to collect the smartphone’s GNSS measurements and PPP-B2b orbit/clock corrections. The results illustrated that the hybrid weight function led to 5.13%, 18.00%, and 15.15% positioning improvements compared to the results of the C/N<sub>0</sub>-dependent model in the east, north, and vertical components, and it exhibited improvements of 71.10%, 72.53%, and 53.93% compared to the results of the satellite-elevation-angle-dependent model. Moreover, the mixed-frequency measurement PPP model could also provide positioning improvements of about 14.63%, 19.99%, and 9.21%. On average, the presented smartphone PPP model can bring about 76.64% and 59.84% positioning enhancements in the horizontal and vertical components.
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spelling doaj-art-4147d937b81c4037bb1c5995d46ce18a2025-08-20T03:50:21ZengMDPI AGApplied Sciences2076-34172025-06-011513716910.3390/app15137169BDS-PPP-B2b-Based Smartphone Precise Positioning Model Enhanced by Mixed-Frequency Data and Hybrid Weight FunctionZhouzheng Gao0Zhixiong Wu1Shiyu Liu2Cheng Yang3School of Land Science and Technology, China University of Geosciences Beijing, Beijing 100083, ChinaSchool of Land Science and Technology, China University of Geosciences Beijing, Beijing 100083, ChinaSchool of Land Science and Technology, China University of Geosciences Beijing, Beijing 100083, ChinaSchool of Land Science and Technology, China University of Geosciences Beijing, Beijing 100083, ChinaCompared to high-cost hardware-based Global Navigation Satellite System (GNSS) positioning techniques, smartphone-based precise positioning technology plays an important role in applications such as the Internet of Things (IoT). Since Google released the Nougat version of Android in 2016, this has provided a new method for achieving high-accuracy positioning solutions with a smartphone. However, two factors are limiting smartphone-based high-accuracy applications, namely, real-time precise orbit/clock products without the internet and the quality-adaptive precise point positioning (PPP) model. To overcome these two factors, we introduce BDS PPP-B2b orbit/clock corrections and a hybrid weight function (based on C/N<sub>0</sub> and satellite elevation) into smartphone real-time PPP. To validate the performance of such a method, two sets of field tests were arranged to collect the smartphone’s GNSS measurements and PPP-B2b orbit/clock corrections. The results illustrated that the hybrid weight function led to 5.13%, 18.00%, and 15.15% positioning improvements compared to the results of the C/N<sub>0</sub>-dependent model in the east, north, and vertical components, and it exhibited improvements of 71.10%, 72.53%, and 53.93% compared to the results of the satellite-elevation-angle-dependent model. Moreover, the mixed-frequency measurement PPP model could also provide positioning improvements of about 14.63%, 19.99%, and 9.21%. On average, the presented smartphone PPP model can bring about 76.64% and 59.84% positioning enhancements in the horizontal and vertical components.https://www.mdpi.com/2076-3417/15/13/7169smartphone precise point positioning (PPP)PPP-B2b servicehybrid weight functionmixed-frequency measurements
spellingShingle Zhouzheng Gao
Zhixiong Wu
Shiyu Liu
Cheng Yang
BDS-PPP-B2b-Based Smartphone Precise Positioning Model Enhanced by Mixed-Frequency Data and Hybrid Weight Function
Applied Sciences
smartphone precise point positioning (PPP)
PPP-B2b service
hybrid weight function
mixed-frequency measurements
title BDS-PPP-B2b-Based Smartphone Precise Positioning Model Enhanced by Mixed-Frequency Data and Hybrid Weight Function
title_full BDS-PPP-B2b-Based Smartphone Precise Positioning Model Enhanced by Mixed-Frequency Data and Hybrid Weight Function
title_fullStr BDS-PPP-B2b-Based Smartphone Precise Positioning Model Enhanced by Mixed-Frequency Data and Hybrid Weight Function
title_full_unstemmed BDS-PPP-B2b-Based Smartphone Precise Positioning Model Enhanced by Mixed-Frequency Data and Hybrid Weight Function
title_short BDS-PPP-B2b-Based Smartphone Precise Positioning Model Enhanced by Mixed-Frequency Data and Hybrid Weight Function
title_sort bds ppp b2b based smartphone precise positioning model enhanced by mixed frequency data and hybrid weight function
topic smartphone precise point positioning (PPP)
PPP-B2b service
hybrid weight function
mixed-frequency measurements
url https://www.mdpi.com/2076-3417/15/13/7169
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