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Special Issue "Sensor Fusion and Signal Processing"

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

Deadline for manuscript submissions: 31 January 2021.

Special Issue Editor

Dr. Raquel Caballero-Aguila
Website
Guest Editor
Departamento de Estadística, Universidad de Jaén, Paraje Las Lagunillas, 23071 Jaén, Spain
Interests: stochastic dynamical systems; random signal estimation; fusion estimation algorithms; discrete-time stochastic systems with network-induced uncertainties

Special Issue Information

Dear Colleagues,

Generally speaking, sensor fusion techniques combine data and knowledge from multiple sources of information to achieve better (less expensive, more accurate, etc.) inferences than those that would be deduced from an individual sensor. Signal processing algorithms for preprocessing sensor data are then needed, together with precise mathematical models (to describe the relation between the sensor outputs and the quantity of interest) and efficient fusion algorithms (to combine the information from the individual sensors). In recent decades, sensor fusion has become an interesting and multidisciplinary topic with applications in several fields, since any task involving estimation problems from multiple sources of information can benefit from the use of sensor fusion methodologies.

Particularly, signal estimation problems in sensor networks constitute a fertile research field with an active progress due to the great number and variety of applications of networked systems in different contexts, such as data acquisition and processing, target tracking and localization, communication, etc. Usually, in practice, network sensors may randomly fail, collapse or suffer communication interferences, so it is necessary to design estimation methods that take into account these random restrictions.

This Special Issue aims at gathering the most recent advances and latest approaches of all topics within the broad field of the fundamentals and applications of sensor fusion and signal processing. Contributions from both theoretical and application sides are welcome, and we also accept survey/tutorial manuscripts.

Potential topics include (but are not limited to):

  • Signal estimation in sensor networks;
  • Information fusion techniques and applications;
  • Fusion estimation algorithms;
  • Sensor fusion for detection;
  • Control systems and sensor fusion;
  • Sensor fusion for automotive applications;
  • Target tracking, fusion and control;
  • Signal and image processing.

Dr. Raquel Caballero-Aguila
Guest Editor

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 papers will be 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 2000 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

  • Multisensor information fusion
  • Fusion estimation algorithms
  • Signal estimation in sensor networks
  • Random uncertainties over sensor networks
  • Processing of sensor data
  • Signal processing
  • Image processing

Published Papers (1 paper)

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Research

Open AccessArticle
Ultimately Bounded Filtering for Time-Delayed Nonlinear Stochastic Systems with Uniform Quantizations under Random Access Protocol
Sensors 2020, 20(15), 4134; https://doi.org/10.3390/s20154134 - 25 Jul 2020
Abstract
This paper investigates the ultimately bounded filtering problem for a kind of time-delay nonlinear stochastic systems with random access protocol (RAP) and uniform quantization effects (UQEs). In order to reduce the occurrence of data conflicts, the RAP is employed to regulate the information [...] Read more.
This paper investigates the ultimately bounded filtering problem for a kind of time-delay nonlinear stochastic systems with random access protocol (RAP) and uniform quantization effects (UQEs). In order to reduce the occurrence of data conflicts, the RAP is employed to regulate the information transmissions over the shared communication channel. The scheduling behavior of the RAP is characterized by a Markov chain with known transition probabilities. On the other hand, the measurement outputs are quantized by the uniform quantizer before being transmitted via the communication channel. The objective of this paper is to devise a nonlinear filter such that, in the simultaneous presence of RAP and UQEs, the filtering error dynamics is exponentially ultimately bounded in mean square (EUBMS). By resorting to the stochastic analysis technique and the Lyapunov stability theory, sufficient conditions are obtained under which the desired nonlinear filter exists, and then the filter design algorithm is presented. At last, two simulation examples are given to validate the proposed filtering strategy. Full article
(This article belongs to the Special Issue Sensor Fusion and Signal Processing)
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