Smartphone Positioning, Navigation and Timing: Advances and Challenges

Topic Information

Dear Colleagues,

As you are all aware, the capabilities of smartphones have greatly evolved over the past decade. With increased computing power and the accessibility to observations collected by various embedded systems, such as an integrated global navigation satellite system (GNSS) chipset, microelectromechanical system (MEMS) inertial sensors, magnetometers, barometers, and high-resolution cameras, smartphones are now a powerful platform for pedestrian and automotive navigation as well as for a wide variety of other applications such as mobile mapping, health monitoring, gaming, and more.

However, one of the major challenges of using smartphones for all these tasks and applications is the quality and availability of their sensors' measurements in various environments. In particular, in positioning applications, the errors affecting these measurements can significantly degrade the achievable performance in terms of accuracy, especially in scenarios where GNSS signals are impaired or unavailable, such as deep urban canyons or indoors. In addition, the flip side of the coin is that most of these devices are gathered in urban areas where GNSS is mostly susceptible to multipath interference and occlusions. This combination makes smartphones and mobile devices to a large extent a favorable breeding ground for innovative and disruptive solutions.

We look forward to receiving contributions that focus on the use of smartphones for positioning and all related applications using GNSS and/or other integrated sensors. We are particularly interested in original papers that address innovative techniques, methods, and algorithms for navigation using smartphones in GNSS-degraded or -denied scenarios, such as in the presence of urban canyons or in indoor environments.

Topics of interest include (but are not limited to):

• Pedestrian–automotive navigation and situational awareness in challenging environments (e.g., deep urban canyons and tunnels);
• Approaches, methods, and algorithms for positioning and related applications that utilize one or more sensors embedded in modern smartphones, such as GNSS, UWB receivers, IMU, magnetometers, barometers, and electro-optical sensors;
• Advanced estimation algorithms and sensor fusion methods including innovative filter designs, the use of motion constraints, and map aiding;
• Real Time Kinematic (RTK) and Precise Point Positioning (PPP) in smartphones
• High-precision GNSS processing using carrier phase measurements, such as robust carrier phase tracking and fast ambiguity resolution; 
• Signal of opportunities (SoO)-based positioning and navigation using smartphones;
• Artificial intelligence and machine-learning-based methods for positioning and fault detection;
• Advanced GNSS interference detection and mitigation solutions based on raw GNSS measurements;
• Approaches, methods, and algorithms for collaborative positioning and navigation among smartphones and mobile devices.

Dr. Vincenzo Capuano
Prof. Dr. Fabio Dovis
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Electronics electronics	2.6	5.3	2012	16.4 Days	CHF 2400	Submit
Geomatics geomatics	-	-	2021	22.1 Days	CHF 1000	Submit
Remote Sensing remotesensing	4.2	8.3	2009	23.9 Days	CHF 2700	Submit
Sensors sensors	3.4	7.3	2001	18.6 Days	CHF 2600	Submit
Smart Cities smartcities	7.0	11.2	2018	28.4 Days	CHF 2000	Submit
Technologies technologies	4.2	6.7	2013	21.1 Days	CHF 1600	Submit

MDPI Topics is cooperating with Preprints.org and has built a direct connection between MDPI journals and Preprints.org. Authors are encouraged to enjoy the benefits by posting a preprint at Preprints.org prior to publication:

1. Immediately share your ideas ahead of publication and establish your research priority;
2. Protect your idea from being stolen with this time-stamped preprint article;
3. Enhance the exposure and impact of your research;
4. Receive feedback from your peers in advance;
5. Have it indexed in Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (3 papers)

Order results

Content type Publication Date

Result details

Normal Extended Compact

Journals

All Electronics Geomatics Remote Sensing Sensors Smart Cities Technologies

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

Error

Oops... you haven't selected anything for export.

Ok

clear

28 pages, 22621 KiB  

Open AccessArticle

A Ray-Tracing-Based Single-Site Localization Method for Non-Line-of-Sight Environments

by Shuo Hu, Lixin Guo and Zhongyu Liu

Sensors 2024, 24(24), 7925; https://doi.org/10.3390/s24247925 - 11 Dec 2024

Viewed by 675

Abstract

Localization accuracy in non-line-of-sight (NLOS) scenarios is often hindered by the complex nature of multipath propagation. Traditional approaches typically focus on NLOS node identification and error mitigation techniques. However, the intricacies of NLOS localization are intrinsically tied to propagation challenges. In this paper, [...] Read more.

Localization accuracy in non-line-of-sight (NLOS) scenarios is often hindered by the complex nature of multipath propagation. Traditional approaches typically focus on NLOS node identification and error mitigation techniques. However, the intricacies of NLOS localization are intrinsically tied to propagation challenges. In this paper, we propose a novel single-site localization method tailored for complex multipath NLOS environments, leveraging only angle-of-arrival (AOA) estimates in conjunction with a ray-tracing (RT) algorithm. The method transforms NLOS paths into equivalent line-of-sight (LOS) paths through the generation of generalized sources (GSs) via ray tracing. A novel weighting mechanism for GSs is introduced, which, when combined with an iteratively reweighted least squares (IRLS) estimator, significantly improves the localization accuracy of non-cooperative target sources. Furthermore, a multipath similarity displacement matrix (MSDM) is incorporated to enhance accuracy in regions with pronounced multipath fluctuations. Simulation results validate the efficacy of the proposed algorithm, achieving localization performance that approaches the Cramér–Rao lower bound (CRLB), even in challenging NLOS scenarios. Full article

(This article belongs to the Topic Smartphone Positioning, Navigation and Timing: Advances and Challenges)

►▼ Show Figures

Figure 1

22 pages, 10007 KiB  

Open AccessArticle

Deep Learning-Based Emergency Rescue Positioning Technology Using Matching-Map Images

by Juil Jeon, Myungin Ji, Jungho Lee, Kyeong-Soo Han and Youngsu Cho

Remote Sens. 2024, 16(21), 4014; https://doi.org/10.3390/rs16214014 - 29 Oct 2024

Cited by 1 | Viewed by 826

Abstract

Smartphone-based location estimation technology is becoming increasingly important across various fields. Accurate location estimation plays a critical role in life-saving efforts during emergency rescue situations, where rapid response is essential. Traditional methods such as GPS often face limitations in indoors or in densely [...] Read more.

Smartphone-based location estimation technology is becoming increasingly important across various fields. Accurate location estimation plays a critical role in life-saving efforts during emergency rescue situations, where rapid response is essential. Traditional methods such as GPS often face limitations in indoors or in densely built environments, where signals may be obstructed or reflected, leading to inaccuracies. Similarly, fingerprinting-based methods rely heavily on existing infrastructure and exhibit signal variability, making them less reliable in dynamic, real-world conditions. In this study, we analyzed the strengths and weaknesses of different types of wireless signal data and proposed a new deep learning-based method for location estimation that comprehensively integrates these data sources. The core of our research is the introduction of a ‘matching-map image’ conversion technique that efficiently integrates LTE, WiFi, and BLE signals. These generated matching-map images were applied to a deep learning model, enabling highly accurate and stable location estimates even in challenging emergency rescue situations. In real-world experiments, our method, utilizing multi-source data, achieved a positioning success rate of 85.27%, which meets the US FCC’s E911 standards for location accuracy and reliability across various conditions and environments. This makes the proposed approach particularly well-suited for emergency applications, where both accuracy and speed are critical. Full article

(This article belongs to the Topic Smartphone Positioning, Navigation and Timing: Advances and Challenges)

►▼ Show Figures

Figure 1

24 pages, 6502 KiB  

Open AccessArticle

Urban Road Surface Condition Sensing from Crowd-Sourced Trajectories Based on the Detecting and Clustering Framework

by Haiyang Lyu, Qiqi Zhong, Yu Huang, Jianchun Hua and Donglai Jiao

Sensors 2024, 24(13), 4093; https://doi.org/10.3390/s24134093 - 24 Jun 2024

Cited by 1 | Viewed by 1151

Abstract

Roads play a crucial role in urban transportation by facilitating the movement of materials within a city. The condition of road surfaces, such as damage and road facilities, directly affects traffic flow and influences decisions related to urban transportation maintenance and planning. To [...] Read more.

Roads play a crucial role in urban transportation by facilitating the movement of materials within a city. The condition of road surfaces, such as damage and road facilities, directly affects traffic flow and influences decisions related to urban transportation maintenance and planning. To gather this information, we propose the Detecting and Clustering Framework for sensing road surface conditions based on crowd-sourced trajectories, utilizing various sensors (GPS, orientation sensors, and accelerometers) found in smartphones. Initially, smartphones are placed randomly during users’ travels on the road to record the road surface conditions. Then, spatial transformations are applied to the accelerometer data based on attitude readings, and heading angles are computed to store movement information. Next, the feature encoding process operates on spatially adjusted accelerations using the wavelet scattering transformation. The resulting encoding results are then input into the designed LSTM neural network to extract bump features of the road surface (BFRSs). Finally, the BFRSs are represented and integrated using the proposed two-stage clustering method, considering distances and directions. Additionally, this procedure is also applied to crowd-sourced trajectories, and the road surface condition is computed and visualized on a map. Moreover, this method can provide valuable insights for urban road maintenance and planning, with significant practical applications. Full article

(This article belongs to the Topic Smartphone Positioning, Navigation and Timing: Advances and Challenges)

►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

 

Error

Oops... you haven't selected anything for export.

Ok

clear

Displaying articles 1-3