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Applied Sciences
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AI-PNT: Artificial Intelligence Applications in Position, Navigation and Timing
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AI-PNT: Artificial Intelligence Applications in Position, Navigation and Timing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 1692

Special Issue Editors

Dr. Changhui Jiang

E-Mail Website
Guest Editor
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Interests: hyperspectral LiDAR; GNSS; INS; Kalman filter; mobile mapping
Special Issues, Collections and Topics in MDPI journals
Dr. Xiang Wu

E-Mail Website
Guest Editor
School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China
Interests: neural networks; pattern recognition; timing analysis; machine learning
Dr. Yafeng Li

E-Mail Website
Guest Editor
School of Automation, Beijing Information Science and Technology University, Beijing 100192, China
Interests: GNSS; GNSS signal processing; GNSS SDR
Dr. Feng Zhou

E-Mail Website
Guest Editor
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
Interests: multi-GNSS PPP-RTK; real-time atmospheric modeling; multi-sensor integration and data processing
Dr. Yuwei Chen

E-Mail Website
Guest Editor
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, 02430 Masala, Finland
Interests: hyperspectral LiDAR; mobile mapping
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Position, navigation and timing (PNT) information is essential for many applications, e.g., in smartphones, shared bicycles, and autonomous driving. Currently, Global Satellite Navigation System (GNSS) is the dominant source of PNT information. However, GNSS cannot work optimally in signal channeling environments, i.e., tunnels, indoor; multipath and non-line-of-sight (NLOS) also degrade its position accuracy in urban areas. Artificial intelligence (AI) and machine learning (ML) have great potential for improving the performance of GNSS in signal challenging environments.

Inertial Navigation System (INS) is a totally self-contained navigation system that processes inertial measurement unit (IMU) data to generate positional information, though its accuracy is degraded by measurement noise. AI or ML methods can help to improve the accuracy of INS position information.

Artificial intelligence (AI) and machine learning (ML) methods have brought enormous changes in many applications. AI and ML also have great potential in PNT applications and are not limited to GNSS and INS. This Special Issue aims to provide a platform for researchers to publish innovative work on the advanced technologies for AI-PNT. Specifically, we invite contributions concerning the following topics:

  • AI supported GNSS, i.e., multipath and NLOS identification, mitigation, or correction;
  • AI-LiDAR/Visual SLAM;
  • AI-supported GNSS interference and spoofing detection;
  • AI-based inertial measurement unit (IMU) signal processing;
  • AI-pedestrian dead reckoning;
  • AI-IMU calibration;
  • AI RTK/PPP;
  • Intelligent position systems for mobile devices, i.e., smartphones;
  • Other innovative works on AI-PNT.

Dr. Changhui Jiang
Dr. Xiang Wu
Dr. Yafeng Li
Dr. Feng Zhou
Dr. Yuwei Chen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • GNSS
  • PNT
  • artificial intelligence
  • RTK
  • PPP
  • SLAM
  • IMU

Published Papers (1 paper)

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Research

16 pages, 4468 KiB  
Article
Seabed Terrain-Aided Navigation Algorithm Based on Combining Artificial Bee Colony and Particle Swarm Optimization
by Dan Wang, Liqiang Liu, Yueyang Ben, Pingan Dai and Jiancheng Wang
Appl. Sci. 2023, 13(2), 1166; https://doi.org/10.3390/app13021166 - 15 Jan 2023
Cited by 1 | Viewed by 960
Abstract
Position errors of inertial navigation systems (INS) increase over time after long-term voyages of the autonomous underwater vehicle. Terrain-aided navigation (TAN) can effectively reduce the accumulated error of the INS. However, traditional TAN algorithms require a long positioning time and need better positioning [...] Read more.
Position errors of inertial navigation systems (INS) increase over time after long-term voyages of the autonomous underwater vehicle. Terrain-aided navigation (TAN) can effectively reduce the accumulated error of the INS. However, traditional TAN algorithms require a long positioning time and need better positioning accuracy, and nonmatching and mismatching are prone to occur, especially when the initial position error is large. To solve this problem, a new algorithm combining the artificial bee colony (ABC) and particle swarm optimization (PSO) was proposed according to the principle of terrain matching, to improve the matching effect. Considering that PSO easily falls into a local optimum, the acceleration factor and inertia weight of PSO were improved. The improved PSO was called WAPSO. ABC was introduced based on WAPSO and could help WAPSO escape local optimum. The final algorithm was termed ABC search-based WAPSO (F-WAPSO). During the continuous iteration of particles, F-WAPSO seeks the optimal position for the particles. Simulation tests show that F-WAPSO can effectively improve the matching accuracy. When the initial position error is 1000 m, the matching error can be reduced to 93.5 m, with a matching time of only 13.7 s. Full article
(This article belongs to the Special Issue AI-PNT: Artificial Intelligence Applications in Position, Navigation and Timing)
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