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Navigation Systems Based on Artificial Neural Networks
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Navigation Systems Based on Artificial Neural Networks

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: closed (20 July 2023) | Viewed by 11046

Special Issue Editors

Prof. Dr. Dah-Jing Jwo

E-Mail Website
Guest Editor
Department of Communications, Navigation and Control Engineering, National Taiwan Ocean University, 2 Pei-Ning Rd., Keelung 202301, Taiwan
Interests: GPS navigation; multisensor integrated navigation; estimation theory and applications; artificial intelligence; guidance, navigation and control (GNC) systems
Special Issues, Collections and Topics in MDPI journals
Dr. Anna Guerra

E-Mail Website
Guest Editor
Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Viale dell’Università 50, 47522 Cesena, Italy
Interests: signal processing; radio localization and tracking; autonomous systems

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to present recent developments and provide a forum for the dissemination of works that exploit the latest works on any problem related to navigation system algorithms and techniques using the art and science of artificial neural networks (ANNs) for accurate and reliable ubiquitous positioning and seamless navigation solutions.

Navigation systems play an important role in many applications. Typical examples include global navigation satellite systems (GNSSs), inertial navigation systems (INSs), GNSS/INS integration, comparative/terrain reference navigation, Wi-Fi/UWB/BLE, visual scene recognition, LiDAR simultaneous localization and mapping (SLAM), and visual SLAM, among others, for various outdoor and indoor applications. There are several challenges associated with existing navigation system designs. In navigation system designs, techniques have been developed for numerous challenges, such as the high dynamics scenarios, uncertainties posed by the system and measurement models, non-Gaussian distributed errors and noises, GNSS-denied/challenged environments, severe multipath interference/urban areas, etc. Many aspects are involved in performance enhancement, including precise sensor modeling, adaptive and robust estimation/filtering, uncertainty estimation of sensors/measurement selections, artificial intelligence, multisensor integration and sensor fusion. Incorporation of the ANN including machine learning and deep learning, context-aware computing, and predictive analytics, provides new potential to tackle these challenges and resolve existing problems.

Recent developments and original research articles addressing advanced technologies and exploring new algorithms for navigation system design based on ANN-related algorithms will be considered for publication in this Special Issue.

Prof. Dr. Dah-Jing Jwo
Dr. Anna Guerra
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

  • ubiquitous positioning
  • seamless navigation
  • artificial neural networks
  • sensor modeling
  • adaptive and robust estimation/filtering
  • uncertainty estimation
  • machine learning and deep learning
  • context-awareness computing
  • predictive analytics

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Published Papers (2 papers)

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Research

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26 pages, 1961 KiB  
Article
Anomaly Detection in Autonomous Deep-Space Navigation via Filter Bank Gating Networks
by Daniel P. Lubey and Todd A. Ely
Appl. Sci. 2022, 12(21), 11161; https://doi.org/10.3390/app122111161 - 3 Nov 2022
Viewed by 1296
Abstract
This study investigates methods for autonomous navigation of a deep-space spacecraft where one-way radiometric and on-board optical information are fused to create a fully informed state estimate. The specific focus is on using filter bank methods (i.e., Multiple Model Estimation [MME] and Mixture [...] Read more.
This study investigates methods for autonomous navigation of a deep-space spacecraft where one-way radiometric and on-board optical information are fused to create a fully informed state estimate. The specific focus is on using filter bank methods (i.e., Multiple Model Estimation [MME] and Mixture of Experts [MoE]) to detect when measurement and/or dynamical mis-modeling occurs. We develop a new χ2-based gating network for a filter bank that may be used to identify poorly performing filters (i.e., those with low weights), which may be used as a signal for mis-modeling in the system. In addition to defining and deriving this new weighting scheme, numerical simulations based on NASA’s InSight mission demonstrate this new algorithm’s performance with and without measurement and dynamical mis-modeling present. Full article
(This article belongs to the Special Issue Navigation Systems Based on Artificial Neural Networks)
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Review

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33 pages, 5262 KiB  
Review
Artificial Neural Networks for Navigation Systems: A Review of Recent Research
by Dah-Jing Jwo, Amita Biswal and Ilayat Ali Mir
Appl. Sci. 2023, 13(7), 4475; https://doi.org/10.3390/app13074475 - 31 Mar 2023
Cited by 20 | Viewed by 8923
Abstract
Several machine learning (ML) methodologies are gaining popularity as artificial intelligence (AI) becomes increasingly prevalent. An artificial neural network (ANN) may be used as a “black-box” modeling strategy without the need for a detailed system physical model. It is more reasonable to solely [...] Read more.
Several machine learning (ML) methodologies are gaining popularity as artificial intelligence (AI) becomes increasingly prevalent. An artificial neural network (ANN) may be used as a “black-box” modeling strategy without the need for a detailed system physical model. It is more reasonable to solely use the input and output data to explain the system’s actions. ANNs have been extensively researched, as artificial intelligence has progressed to enhance navigation performance. In some circumstances, the Global Navigation Satellite System (GNSS) can offer consistent and dependable navigational options. A key advancement in contemporary navigation is the fusion of the GNSS and inertial navigation system (INS). Numerous strategies have been put out recently to increase the accuracy for jamming, GNSS-prohibited environments, the integration of GNSS/INS or other technologies by means of a Kalman filter as well as to solve the signal blockage issue in metropolitan areas. A neural-network-based fusion approach is suggested to address GNSS outages. The overview, inquiry, observation, and performance evaluation of the present integrated navigation systems are the primary objectives of the review. The important findings in ANN research for use in navigation systems are reviewed. Reviews of numerous studies that have been conducted to investigate, simulate, and integrate navigation systems in order to produce accurate and dependable navigation solutions are offered. Full article
(This article belongs to the Special Issue Navigation Systems Based on Artificial Neural Networks)
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