Recent Advancements in Indoor Positioning and Localization

Dear Colleagues,

This era is marked by the rise of mobile devices, which has led to the inception and initiation of many intuitive and diverse services related to such devices, including location-based services (LBSs), mobile applications, online customer services, etc. LBSs are a major part of such services and are expected to grow further in the coming years. LBSs, although offered both outdoors and indoors, still face numerous challenges for indoor environments, especially complex environments, artificially impaired scenarios where the positioning accuracy of the Global Positioning System (GPS) is severely affected. Unlike GPS, which serves as a de facto technology for outdoor location services, no such counterpart is available for indoor environments. Recent years have seen wide research in machine and deep learning techniques, and research and academia are adopting these algorithms and models for indoor positioning and localization. Additionally, the launch of 5G has opened new dimensions for cellular-based indoor positioning, and researchers are working on various techniques to provide unprecedented position information for complex indoor environments. Wireless local area networks (WLANs), Bluetooth, magnetic-field-based positioning systems, and pedestrian dead reckoning are a few alternatives that can be readily adapted for indoor positioning.

Existing indoor positioning algorithms and techniques do no come meet the accuracy requirements of LBSs. Some of these are affected by their inherent limitations, while others are limited by real-time changes in the environment. To meet the standards and requirements of LBS accuracy, advanced algorithms, custom models, hybrid solutions, and novel fusion frameworks should be contrived which can provide unparalleled accuracy and prove their efficacy for indoor positioning and localization. Machine learning techniques and deep learning models recently proved the potential to be adopted for indoor positioning and opened new possibilities to refine indoor location information. However, such methods are yet to be experimented upon to realize their full potential.

This Special Issue will focus on the publication of high-quality research articles and review papers that articulate recent advancements in indoor positioning and localization. Novel architectures, innovative algorithms, and intuitive models are deemed valuable; however, the articles with experimental results will be highly regarded and appreciated. Recent deep learning architectures and models which aim to enhance the localization accuracy are desired as well.

Topics of interest include, but are not limited to, the following:

• Indoor positioning based on cellular networks with 4G and 5G;
• WLAN-based indoor positioning;
• Pedestrian dead reckoning systems;
• Magnetic-field-based positioning systems;
• Hybrid systems with sensor fusion models;
• Deep learning algorithms based on DNNs, CNNs, LSTMS, etc.;
• Advanced learning approaches for positioning;
• Deep learning architectures;
• Advanced models for user walk trajectory;
• Smartphone-sensors-based indoor positioning techniques;
• Recent developments in filtering approaches (e.g., KF, PF, EKF);
• Simultaneous localization and mapping for smartphone-based indoor positioning.

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. Electronics 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.

• indoor positioning;
• indoor localization;
• PDR;
• filter approaches;
• SLAM;
• magnetic field;
• deep learning algorithms;
• deep learning architectures;
• reinforcement learning.