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Intelligent Wireless Sensing and Positioning
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Intelligent Wireless Sensing and Positioning

A special issue of Signals (ISSN 2624-6120).

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 12961

Special Issue Editors

Dr. Zan Li

E-Mail Website
Guest Editor
College of Communication Engineering, Jilin University, Changchun 130012, China
Interests: wireless communications; indoor positioning; signal processing; wireless sensing
Prof. Dr. Torsten Braun

E-Mail Website
Co-Guest Editor
Institute for Computer Science, Universität Bern, Bern, Switzerland
Interests: Computer Science
Dr. Dayang Sun

E-Mail Website
Co-Guest Editor
College of Communication Engineering, Jilin University, Changchun, China
Interests: wireless sensor networks; localization theory and wireless communication

Special Issue Information

Dear Colleagues,

With the rapid development of communication techniques, emerging wireless communication techniques like mmWave and MIMO have become popular to support applications which demand a high data rate, such as new standard WiFi and 5G networks. In addition to communication, these wireless signals have been investigated to support sensing with high accuracy, such as activity sensing, people positioning, and object detection. Hence, integrated sensing and communication have become an emerging topic in the future 6G networks. Traditional sensing approaches such as LIDAR and computer vision suffer from the drawbacks of optical imaging and are strongly affected by the weather conditions of surrounding environments. Compared with these traditional sensing approaches, wireless sensing is more suitable for ubiquitous sensing in all weather. However, wireless sensing (e.g., based on WiFi and mmWave) is still very challenging because it is prone to multipath propagation and sparse point clouds. In this Special Issue, we aim to organize a forum for the presentation of new, improved, and developing techniques in the general area of wireless sensing and positioning.

Topics of interest for this Special Issue include but are not limited to:

  • Activity recognition based on wireless signals;
  • Object detection based on wireless signals;
  • Passive positioning and tracking based on wireless signals;
  • Active positioning and tracking based on wireless signals;
  • Integrated sensing and communication based on wireless signals;
  • Indoor positioning based on wireless signals;
  • SLAM techniques based on wireless signals;
  • Fusion sensing based on wireless signals and other sensing techniques;
  • Others related to wireless sensing.

Dr. Zan Li
Prof. Dr. Torsten Braun
Dr. Dayang Sun
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. Signals is an international peer-reviewed open access quarterly 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 1000 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.

Published Papers (5 papers)

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Research

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16 pages, 2388 KiB  
Article
An Improved d-MP Algorithm for Reliability of Logistics Delivery Considering Speed Limit of Different Roads
by Wei-Chang Yeh, Chia-Ling Huang and Haw-Sheng Wu
Signals 2022, 3(4), 895-910; https://doi.org/10.3390/signals3040053 - 13 Dec 2022
Viewed by 1079
Abstract
The construction of intelligent logistics by intelligent wireless sensing is a modern trend. Hence, this study uses the multistate flow network (MFN) to explore the actual environment of logistics delivery and to consider the different types of transportation routes available for logistics trucks [...] Read more.
The construction of intelligent logistics by intelligent wireless sensing is a modern trend. Hence, this study uses the multistate flow network (MFN) to explore the actual environment of logistics delivery and to consider the different types of transportation routes available for logistics trucks in today’s practical environment, which have been neglected in previous studies. Two road types, namely highways and slow roads, with different speed limits are explored. The speed of the truck is fast on the highway, so the completion time of the single delivery is, of course, fast. However, it is also because of its high speed that it is subject to many other conditions. For example, if the turning angle of the truck is too large, there will be a risk of the truck overturning, which is a quite serious and important problem that must be included as a constraint. Moreover, highways limit the weight of trucks, so this limit is also included as a constraint. On the other hand, if the truck is driving on a slow road, where its speed is much slower than that of a highway, it is not limited by the turning angle. Nevertheless, regarding the weight capacity of trucks, although the same type of trucks running on slow roads can carry a weight capacity that is higher than the load weight limit of driving on the highway, slow roads also have a load weight limit. In addition to a truck’s aforementioned turning angle and load weight capacity, in today’s logistics delivery, time efficiency is extremely important, so the delivery completion time is also included as a constraint. Therefore, this study uses the improved d-MP method to study the reliability of logistics delivery in trucks driving on two types of roads under constraints to help enhance the construction of intelligent logistics with intelligent wireless sensing. An illustrative example in an actual environment is introduced. Full article
(This article belongs to the Special Issue Intelligent Wireless Sensing and Positioning)
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20 pages, 10620 KiB  
Article
Cross-Scene Sign Language Gesture Recognition Based on Frequency-Modulated Continuous Wave Radar
by Xiaochao Dang, Kefeng Wei, Zhanjun Hao and Zhongyu Ma
Signals 2022, 3(4), 875-894; https://doi.org/10.3390/signals3040052 - 06 Dec 2022
Cited by 1 | Viewed by 1446
Abstract
This paper uses millimeter-wave radar to recognize gestures in four different scene domains. The four scene domains are the experimental environment, the experimental location, the experimental direction, and the experimental personnel. The experiments are carried out in four scene domains, using part of [...] Read more.
This paper uses millimeter-wave radar to recognize gestures in four different scene domains. The four scene domains are the experimental environment, the experimental location, the experimental direction, and the experimental personnel. The experiments are carried out in four scene domains, using part of the data of a scene domain as the training set for training. The remaining data is used as a validation set to validate the training results. Furthermore, the gesture recognition results of known scenes can be extended to unknown stages after obtaining the original gesture data in different scene domains. Then, three kinds of hand gesture features independent of the scene domain are extracted: range-time spectrum, range-doppler spectrum, and range-angle spectrum. Then, they are fused to represent a complete and comprehensive gesture action. Then, the gesture is trained and recognized using the three-dimensional convolutional neural network (CNN) model. Experimental results show that the three-dimensional CNN can fuse different gesture feature sets. The average recognition rate of the fused gesture features in the same scene domain is 87%, and the average recognition rate in the unknown scene domain is 83.1%, which verifies the feasibility of gesture recognition across scene domains. Full article
(This article belongs to the Special Issue Intelligent Wireless Sensing and Positioning)
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9 pages, 2015 KiB  
Article
The Analysis and Verification of Unbiased Estimator for Multilateral Positioning
by Yang Yang, Shihao Sun, Ao Chen, Siyang You, Yuqi Shen, Zhijun Li and Dayang Sun
Signals 2022, 3(3), 497-505; https://doi.org/10.3390/signals3030029 - 12 Jul 2022
Viewed by 1413
Abstract
The ranging error model is generally very complicated in actual ranging technologies. This paper gives an analysis of the biased distance substitution and proposes an unbiased multilateral positioning method to revise the biased substitution, making it an unbiased estimate of the squared distance. [...] Read more.
The ranging error model is generally very complicated in actual ranging technologies. This paper gives an analysis of the biased distance substitution and proposes an unbiased multilateral positioning method to revise the biased substitution, making it an unbiased estimate of the squared distance. An unbiased estimate of the multilateral positioning formula is derived to solve the target node coordinates. Through simulation experiments, it is proved that the algorithm can improve the positioning accuracy, and the improvement is more obvious when the error variance is larger. Experiments using SX1280 also show that the ranging conforms to the biased error model, and the accuracy can be improved by using the unbiased estimator. When the actual experimental error standard deviation is 0.16 m, the accuracy can be improved by 0.15 m. Full article
(This article belongs to the Special Issue Intelligent Wireless Sensing and Positioning)
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18 pages, 3592 KiB  
Article
Activity Recognition Based on Millimeter-Wave Radar by Fusing Point Cloud and Range–Doppler Information
by Yuchen Huang, Wei Li, Zhiyang Dou, Wantong Zou, Anye Zhang and Zan Li
Signals 2022, 3(2), 266-283; https://doi.org/10.3390/signals3020017 - 02 May 2022
Cited by 17 | Viewed by 3830
Abstract
Millimeter-wave radar has demonstrated its high efficiency in complex environments in recent years, which outperforms LiDAR and computer vision in human activity recognition in the presence of smoke, fog, and dust. In previous studies, researchers mostly analyzed either 2D (3D) point cloud or [...] Read more.
Millimeter-wave radar has demonstrated its high efficiency in complex environments in recent years, which outperforms LiDAR and computer vision in human activity recognition in the presence of smoke, fog, and dust. In previous studies, researchers mostly analyzed either 2D (3D) point cloud or range–Doppler information from radar echo to extract activity features. In this paper, we propose a multi-model deep learning approach to fuse the features of both point clouds and range–Doppler for classifying six activities, i.e., boxing, jumping, squatting, walking, circling, and high-knee lifting, based on a millimeter-wave radar. We adopt a CNN–LSTM model to extract the time-serial features from point clouds and a CNN model to obtain the features from range–Doppler. Then we fuse the two features and input the fused feature into the full connected layer for classification. We built a dataset based on a 3D millimeter-wave radar from 17 volunteers. The evaluation result based on the dataset shows that this method has higher accuracy than utilizing the two kinds of information separately and achieves a recognition accuracy of 97.26%, which is about 1% higher than other networks with only one kind of data as input. Full article
(This article belongs to the Special Issue Intelligent Wireless Sensing and Positioning)
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Review

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47 pages, 3554 KiB  
Review
A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
by Constantina Isaia and Michalis P. Michaelides
Signals 2023, 4(1), 90-136; https://doi.org/10.3390/signals4010006 - 28 Jan 2023
Cited by 4 | Viewed by 4142
Abstract
In recent years, tremendous advances have been made in the design and applications of wireless networks and embedded sensors. The combination of sophisticated sensors with wireless communication has introduced new applications, which can simplify humans’ daily activities, increase independence, and improve quality of [...] Read more.
In recent years, tremendous advances have been made in the design and applications of wireless networks and embedded sensors. The combination of sophisticated sensors with wireless communication has introduced new applications, which can simplify humans’ daily activities, increase independence, and improve quality of life. Although numerous positioning techniques and wireless technologies have been introduced over the last few decades, there is still a need for improvements, in terms of efficiency, accuracy, and performance for the various applications. Localization importance increased even more recently, due to the coronavirus pandemic, which made people spend more time indoors. Improvements can be achieved by integrating sensor fusion and combining various wireless technologies for taking advantage of their individual strengths. Integrated sensing is also envisaged in the coming technologies, such as 6G. The primary aim of this review article is to discuss and evaluate the different wireless positioning techniques and technologies available for both indoor and outdoor localization. This, in combination with the analysis of the various discussed methods, including active and passive positioning, SLAM, PDR, integrated sensing, and sensor fusion, will pave the way for designing the future wireless positioning systems. Full article
(This article belongs to the Special Issue Intelligent Wireless Sensing and Positioning)
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