Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
3.4
Journals
Sensors
Special Issues
Advanced Sensing Technologies in Automation and Computer Sciences
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Advanced Sensing Technologies in Automation and Computer Sciences

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 14619

Special Issue Editors

Prof. Dr. Javier Bajo

E-Mail Website
Guest Editor
Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Campus Montegancedo, Boadilla del Monte, 28660 Madrid, Spain
Interests: distributed artificial intelligence; knowledge representation; information fusion; mobile sensing; social computing
Special Issues, Collections and Topics in MDPI journals
Dr. Yun Zhu

E-Mail Website
Guest Editor
School of Computer Science, Shaanxi Normal University, Xi’an 710062, China
Interests: information fusion; sensor network; target tracking
Special Issues, Collections and Topics in MDPI journals
Dr. Emilio Serrano

E-Mail Website
Guest Editor
Department of Artificial Intelligence, Computer Science Technical School, Universidad Politécnica de Madrid, 28660 Madrid, Spain
Interests: software agents; machine learning; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Tiancheng Li

E-Mail Website
Guest Editor
Key Laboratory of Information Fusion Technology, School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
Interests: information fusion; sensor network; positioning; target tracking
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Perception is a key element in automation and computer sciences. Sensing technologies are a fundamental part of perception and data acquisition processes and have notably evolved during recent years. The rapid development of advanced sensors and relevant sensing technologies has facilitated the booming of the era of big data and artificial intelligence, providing a foundation for new paradigms in information perception and data acquisition. Research on intelligent sensing systems, technologies, algorithms, and approaches has attracted considerable attention, promoting applications in intelligent transportation, autonomous vehicles, advanced robots, wireless sensor networks, and the Internet of Things.

This Special Issue is associated with the 10th International Conference on Control, Automation and Information Sciences (https://www.iccais2021.com/) to be held in Xi’an, China, 14th–17th October 2021. Accepted papers will be invited for poster or oral presentations at the conference.

This Special Issue fits with the scope of Sensors, paying attention to recent advances and trends in sensing technologies’ algorithms, and approaches in the field of automation engineering and computer/information sciences, highlighting the importance of sensing technologies in information perception, acquisition, mining, and fusion processes. It seeks the latest findings from theoretical research and ongoing projects. Additionally, review articles that provide readers with current research trends and solutions are also welcome. The potential topics include, but are not limited to, the following:

  • Knowledge graphs for sensing technologies;
  • Machine learning for sensing technologies;
  • Software platforms and frameworks for mobile sensing;
  • Advanced sensing technologies;
  • Artificial intelligence for sensing technologies;
  • Intelligent control with sensing technologies;
  • Data mining and information fusion;
  • Sensor data acquisition and perception;
  • Data science for sensing technologies;
  • Sensor management;
  • Intelligent data clustering/fusion;
  • Sensor network systems and applications;
  • Intelligent transportation and autonomous vehicles.

This special issue is focused more on sensors. Papers focus on Automation may choose our Joint Special Issue in Automation (ISSN 2673-4052).

Prof. Dr. Javier Bajo
Dr. Yun Zhu
Dr. Emilio Serrano
Prof. Dr. Tiancheng Li
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. Sensors 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 2600 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.

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

15 pages, 3061 KiB  
Article
A Novel Clutter Suppression Method Based on Sparse Bayesian Learning for Airborne Passive Bistatic Radar with Contaminated Reference Signal
by Jipeng Wang, Jun Wang, Yun Zhu and Dawei Zhao
Sensors 2021, 21(20), 6736; https://doi.org/10.3390/s21206736 - 11 Oct 2021
Cited by 1 | Viewed by 2369
Abstract
The novel sensing technology airborne passive bistatic radar (PBR) has the problem of being affecting by multipath components in the reference signal. Due to the movement of the receiving platform, different multipath components contain different Doppler frequencies. When the contaminated reference signal is [...] Read more.
The novel sensing technology airborne passive bistatic radar (PBR) has the problem of being affecting by multipath components in the reference signal. Due to the movement of the receiving platform, different multipath components contain different Doppler frequencies. When the contaminated reference signal is used for space–time adaptive processing (STAP), the power spectrum of the spatial–temporal clutter is broadened. This can cause a series of problems, such as affecting the performance of clutter estimation and suppression, increasing the blind area of target detection, and causing the phenomenon of target self-cancellation. To solve this problem, the authors of this paper propose a novel algorithm based on sparse Bayesian learning (SBL) for direct clutter estimation and multipath clutter suppression. The specific process is as follows. Firstly, the space–time clutter is expressed in the form of covariance matrix vectors. Secondly, the multipath cost is decorrelated in the covariance matrix vectors. Thirdly, the modeling error is reduced by alternating iteration, resulting in a space–time clutter covariance matrix without multipath components. Simulation results showed that this method can effectively estimate and suppress clutter when the reference signal is contaminated. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies in Automation and Computer Sciences)
►▼ Show Figures

Figure 1

23 pages, 7340 KiB  
Article
Motion Control of a Gecko-like Robot Based on a Central Pattern Generator
by Qing Han, Feixiang Cao, Peng Yi and Tiancheng Li
Sensors 2021, 21(18), 6045; https://doi.org/10.3390/s21186045 - 9 Sep 2021
Cited by 5 | Viewed by 2494
Abstract
To solve the problem of the motion control of gecko-like robots in complex environments, a central pattern generator (CPG) network model of motion control was designed. The CPG oscillation model was first constructed using a sinusoidal function, resulting in stable rhythm control signals [...] Read more.
To solve the problem of the motion control of gecko-like robots in complex environments, a central pattern generator (CPG) network model of motion control was designed. The CPG oscillation model was first constructed using a sinusoidal function, resulting in stable rhythm control signals for each joint of the gecko-like robot. Subsequently, the gecko-like robot successfully walked, crossed obstacles and climbed steps in the vertical plane, based on stable rhythm control signals. Both simulations and experiments validating the feasibility of the proposed CPG motion control model are presented. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies in Automation and Computer Sciences)
►▼ Show Figures

Figure 1

14 pages, 3892 KiB  
Communication
Reconfigurable Antenna Array Direction Finding System Based on a Fast Search Algorithm
by Chunxi Liu, Dongliang Peng, Zhikun Chen, Yong Wu and Binan Wang
Sensors 2021, 21(14), 4729; https://doi.org/10.3390/s21144729 - 10 Jul 2021
Cited by 3 | Viewed by 2718
Abstract
In a traditional antenna array direction finding system, all the antenna sensors need to work or shut down at the same time, which often leads to signal crosstalk, signal distortion, and other electromagnetic compatibility problems. In addition, the direction-finding algorithm in a traditional [...] Read more.
In a traditional antenna array direction finding system, all the antenna sensors need to work or shut down at the same time, which often leads to signal crosstalk, signal distortion, and other electromagnetic compatibility problems. In addition, the direction-finding algorithm in a traditional system needs a tremendous spectral search, which consumes considerable time. To compensate for these deficiencies, a reconfigurable antenna array direction finding system is established in this paper. This system can dynamically load part or all of the antennas through microwave switches (such as a PIN diode) and conduct a fast direction of arrival (DOA) search. First, the hardware structure of the reconfigurable antenna is constructed. Then, based on the conventional spatial domain search algorithm, an improved transform domain (TD) search algorithm is proposed. The effectiveness of the system has been proven by real experiments, and the advantage of the system has been verified by detailed simulations. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies in Automation and Computer Sciences)
►▼ Show Figures

Figure 1

Review

Jump to: Research

20 pages, 1391 KiB  
Review
Performance Evaluation Metrics and Approaches for Target Tracking: A Survey
by Yan Song, Zheng Hu, Tiancheng Li and Hongqi Fan
Sensors 2022, 22(3), 793; https://doi.org/10.3390/s22030793 - 20 Jan 2022
Cited by 11 | Viewed by 5134
Abstract
Performance evaluation (PE) plays a key role in the design and validation of any target-tracking algorithms. In fact, it is often closely related to the definition and derivation of the optimality/suboptimality of an algorithm such as that all minimum mean-squared error estimators are [...] Read more.
Performance evaluation (PE) plays a key role in the design and validation of any target-tracking algorithms. In fact, it is often closely related to the definition and derivation of the optimality/suboptimality of an algorithm such as that all minimum mean-squared error estimators are based on the minimization of the mean-squared error of the estimation. In this paper, we review both classic and emerging novel PE metrics and approaches in the context of estimation and target tracking. First, we briefly review the evaluation metrics commonly used for target tracking, which are classified into three groups corresponding to the most important three factors of the tracking algorithm, namely correctness, timeliness, and accuracy. Then, comprehensive evaluation (CE) approaches such as cloud barycenter evaluation, fuzzy CE, and grey clustering are reviewed. Finally, we demonstrate the use of these PE metrics and CE approaches in representative target tracking scenarios. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies in Automation and Computer Sciences)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop