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Electronics
Special Issues
Object Positioning and Tracking Technologies: Recent Advances and Future Trends
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Object Positioning and Tracking Technologies: Recent Advances and Future Trends

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 3992

Special Issue Editors

Prof. Dr. Youngok Kim

E-Mail Website
Guest Editor
Electronic Engineering Department, Kwangwoon University, Seoul 01897, Republic of Korea
Interests: sensor networks; ultra-wideband; machine learning; 2D/3D positioning systems
Special Issues, Collections and Topics in MDPI journals
Dr. Zhou Biao

E-Mail Website
Guest Editor
Internet of Things School, Jiangnan University, Wuxi 214122, China
Interests: advanced positioning and tracking algorithms; RFID-based passive positioning schemes; multiple positioning system fusion
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

Following the huge success of the Global Navigation Satellite System (GNSS), which allows users to estimate their position outdoors, various object positioning and tracking technologies have received significant attention recently for indoor or outdoor applications, such as autonomous driving, unmanned vehicles, human–robot collaboration in smart factories, mixed reality, indoor navigation, and security and disaster safety services.

According to positioning or tracking scenarios, current existing technologies fall into two categories: active schemes and passive schemes. In active schemes, an object should carry assisting devices, such as a smart phone, laptop, or active/passive RFID card, while the object does not need to carry any specific devices in passive schemes, which is also called device-free localization.

Most current positioning and tracking systems introduced in the literature for both schemes are based on either one or more than two technologies such as GPS, cameras, Wi-Fi, ZigBee, RFID, Bluetooth, ultra-wideband, FMCW radar, (ultra) sound, infrared, lasers, magnetic field, VLC, or inertial measurement unit. However, researchers need to design the positioning and tracking systems carefully, because each technology has different performance characteristics in positioning accuracy, service coverage, simultaneous multi-object tracking ability, and reliability against interferences. Furthermore, the required accuracy and service coverage are different among the different applications.  Since significant advances have been made recently in image processing through the artificial neural networks, the machine learning technology of artificial neural networks is also employed to overcome the limitations of the conventional positioning and tracking technologies.

In this regard, a strong interest is emerging in recent advances and future trends for object positioning and tracking technologies for both active and passive schemes, as well as seamless tracking methods interoperating with the indoor and outdoor positioning technologies.

This Special Issue will focus on the publication of high-quality research articles and review papers that articulate recent advances and future trends for object positioning and tracking technologies in active or passive schemes.

The topics of interest include but are not limited to:

  • Positioning or tracking technology based on Internet of Things;
  • Positioning technology based on wireless signals such as Wi-Fi, ZigBee, Bluetooth;
  • Passive RFID-based positioning technologies;
  • UWB or FMCW Radar based object positioning or tracking technologies;
  • Indoor and outdoor seamless tracking technologies;
  • Positioning systems based on cellular networks of 4G, 5G, and Beyond 5G;
  • Machine learning and artificial intelligence for enhancing positioning accuracy;
  • Passive tracking systems;
  • GPS-based hybrid positioning or tracking systems;
  • Non-GPS hybrid positioning or tracking technologies;
  • Signal processing for positioning or tracking systems;
  • Pedestrian dead reckoning systems;
  • Magnetic field based positioning systems.

Prof. Dr. Youngok Kim
Dr. Zhou Biao
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. 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.

Keywords

  • positioning
  • tracking
  • device-free localization
  • machine learning
  • sensors
  • hybrid positioning
  • seamless tracking
  • artificial neural networks

Published Papers (2 papers)

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Research

16 pages, 2147 KiB  
Article
Trajectory Recovery Based on Interval Forward–Backward Propagation Algorithm Fusing Multi-Source Information
by Biao Zhou, Xiuwei Wang, Junhao Zhou and Changqiang Jing
Electronics 2022, 11(21), 3634; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11213634 - 7 Nov 2022
Cited by 1 | Viewed by 1245
Abstract
In the tracking scheme in which global navigation satellite system (GNSS) measurement is temporally lost or the sampling frequency is insufficient, dead reckoning based on the inertial measurement unit (IMU) and other location-related information can be fused as a supplement for real-time trajectory [...] Read more.
In the tracking scheme in which global navigation satellite system (GNSS) measurement is temporally lost or the sampling frequency is insufficient, dead reckoning based on the inertial measurement unit (IMU) and other location-related information can be fused as a supplement for real-time trajectory recovery. The tracking scheme based on interval analysis outputs interval results containing the ground truth, which gives it the advantage of convenience in multi-source information fusion. In this paper, a trajectory-recovery algorithm based on interval analysis is proposed, which can conveniently fuse GNSS measurement, IMU data, and map constraints and then output an interval result containing the actual trajectory. In essence, the location-related information such as satellite measurement, inertial data, and map constraints is collected by practical experiments and then converted into interval form. Thereby, the interval-overlapping calculation is performed through forward and backward propagation to accomplish the trajectory recovery. The practical experimental results show that the trajectory recovery accuracy based on the proposed algorithm performs better than the traditional Kalman filter algorithm, and the estimated interval results deterministically contain the actual trajectory. More importantly, the proposed interval algorithm is approved to be convenient to fuse additional location-related information. Full article
(This article belongs to the Special Issue Object Positioning and Tracking Technologies: Recent Advances and Future Trends)
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16 pages, 4641 KiB  
Article
Geometric Midpoint Algorithm for Device-Free Localization in Low-Density Wireless Sensor Networks
by Chao Sun, Biao Zhou, Shangyi Yang and Youngok Kim
Electronics 2021, 10(23), 2924; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10232924 - 25 Nov 2021
Cited by 5 | Viewed by 1943
Abstract
Device-free localization (DFL) is a technique used to track a target transporting no electronic devices. Radiofrequency (RF) tomography based DFL technology in wireless sensor networks has been a popular research topic in recent years. Typically, high-tracking accuracy requires a high-density wireless network which [...] Read more.
Device-free localization (DFL) is a technique used to track a target transporting no electronic devices. Radiofrequency (RF) tomography based DFL technology in wireless sensor networks has been a popular research topic in recent years. Typically, high-tracking accuracy requires a high-density wireless network which limits its application in some resource-limited scenarios. To solve this problem, a geometric midpoint (GM) algorithm based on the computations of simple geometric objects is proposed to realize effective tracking of moving targets in low-density wireless networks. First, we proposed a signal processing method for raw RSS signals collected from wireless links that can detect the fluctuations caused by a moving target effectively. Second, a geometric midpoint algorithm is proposed to estimate the location of the target. Finally, simulations and experiments were performed to validate the proposed scheme. The experimental results show that the proposed GM algorithm outperforms the geometric filter algorithm, which is a state-of-the-art DFL method that yields tracking root-mean-square errors up to 0.86 m and improvements in tracking accuracy up to 67.66%. Full article
(This article belongs to the Special Issue Object Positioning and Tracking Technologies: Recent Advances and Future Trends)
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