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Signal Detection and Processing of Sensor Arrays
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Signal Detection and Processing of Sensor Arrays

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

Deadline for manuscript submissions: 12 December 2024 | Viewed by 1511

Special Issue Editors

Prof. Dr. Ka-fai Cedric Yiu

E-Mail Website
Guest Editor
The Department of Applied Mathematics, The Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong 999077, China
Interests: signal processing; beamforming
Dr. Kit Yan Chan

E-Mail Website
Guest Editor
School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Perth, WA 6102, Australia
Interests: power allocation and energy efficiency for device-to-device communications and mobile communications; artificial intelligence; machine learning and heuristic optimization for computer vision and speech enhancement

Special Issue Information

Dear Colleagues,

Signal detection and processing of sensor arrays involve the acquisition, analysis, and interpretation of signals from multiple sensors to extract useful information. These processes are commonly used in various fields, including telecommunications, radar systems, medical imaging, industrial and environmental monitoring, smart homes, transportation systems, security systems, and many others. The versatility of sensor arrays, which consist of multiple sensors placed in a specific geometric arrangement, allows for a wide range of applications where extracting useful information from multiple sensors is crucial.  Beamforming is a key technique used in sensor arrays. It involves combining the signals received by different sensors with appropriate weights to form a composite signal. It is also essential to align the acquired signals in time to ensure accurate signal processing. Advanced signal processing methods, such as adaptive algorithms and machine learning, are often employed to improve detection performance and extract valuable information from the sensor array data. The design of optimal algorithms for signal detection, as well as the estimation of the parameters of signals obtained from sensor arrays, are also important. This Special Issue aims to collect high-quality research papers and review articles focusing on a broad range of topics related to sensor arrays and their applications. Potential topics include but are not limited to the following:

  • Beamforming algorithms for sensor arrays
  • Synchronization techniques
  • Asynchronous sensor arrays
  • Source localization and DOA estimation
  • Array calibration and configuration
  • Distributed sensor arrays
  • Artificial Intelligence and Machine Learning techniques for sensor arrays
  • Optimization methods for sensor array configurations
  • Data fusion signal synchronization in sensor arrays
  • Sensor array networks and the IoT
  • Implementation of sensor arrays in embedded systems
  • Novel applications of sensor arrays such as communications, robotic control, automotive, automatic manufacturing, transportation, biomedical
  • Real-time analysis for data in sensor array networks

Prof. Dr. Ka-fai Cedric Yiu
Dr. Kit Yan Chan
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.

Keywords

  • beamforming algorithms for sensor arrays
  • synchronization techniques
  • asynchronous sensor arrays
  • source localization and DOA estimation
  • array calibration and configuration
  • distributed sensor arrays
  • artificial intelligence and machine learning techniques for sensor arrays
  • optimization methods for sensor array configurations
  • data fusion signal synchronization in sensor arrays
  • sensor array networks and the IoT
  • implementation of sensor arrays in embedded systems
  • novel applications of sensor arrays such as communications, robotic control, automotive, automatic manufacturing, transportation, biomedical
  • real-time analysis for data in sensor array networks

Published Papers (3 papers)

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Research

13 pages, 4182 KiB  
Article
Scale-Aware Edge-Preserving Full Waveform Inversion with Diffusion Filter for Crosshole Sensor Arrays
by Jixin Yang, Xiao He, Hao Chen, Jiacheng Li and Wenwen Wang
Sensors 2024, 24(9), 2881; https://doi.org/10.3390/s24092881 - 30 Apr 2024
Viewed by 160
Abstract
Full waveform inversion (FWI) is recognized as a leading data-fitting methodology, leveraging the detailed information contained in physical waveform data to construct accurate, high-resolution velocity models essential for crosshole surveys. Despite its effectiveness, FWI is often challenged by its sensitivity to data quality [...] Read more.
Full waveform inversion (FWI) is recognized as a leading data-fitting methodology, leveraging the detailed information contained in physical waveform data to construct accurate, high-resolution velocity models essential for crosshole surveys. Despite its effectiveness, FWI is often challenged by its sensitivity to data quality and inherent nonlinearity, which can lead to instability and the inadvertent incorporation of noise and extraneous data into inversion models. To address these challenges, we introduce the scale-aware edge-preserving FWI (SAEP-FWI) technique, which integrates a cutting-edge nonlinear anisotropic hybrid diffusion (NAHD) filter within the gradient computation process. This innovative filter effectively reduces noise while simultaneously enhancing critical small-scale structures and edges, significantly improving the fidelity and convergence of the FWI inversion results. The application of SAEP-FWI across a variety of experimental and authentic crosshole datasets clearly demonstrates its effectiveness in suppressing noise and preserving key scale-aware and edge-delineating features, ultimately leading to clear inversion outcomes. Comparative analyses with other FWI methods highlight the performance of our technique, showcasing its ability to produce images of notably higher quality. This improvement offers a robust solution that enhances the accuracy of subsurface imaging. Full article
(This article belongs to the Special Issue Signal Detection and Processing of Sensor Arrays)
14 pages, 5680 KiB  
Article
Optimal Microphone Array Placement Design Using the Bayesian Optimization Method
by Yuhan Zhang, Zhibao Li and Ka Fai Cedric Yiu
Sensors 2024, 24(8), 2434; https://doi.org/10.3390/s24082434 - 10 Apr 2024
Viewed by 405
Abstract
In addition to the filter coefficients, the location of the microphone array is a crucial factor in improving the overall performance of a beamformer. The optimal microphone array placement can considerably enhance speech quality. However, the optimization problem with microphone configuration variables is [...] Read more.
In addition to the filter coefficients, the location of the microphone array is a crucial factor in improving the overall performance of a beamformer. The optimal microphone array placement can considerably enhance speech quality. However, the optimization problem with microphone configuration variables is non-convex and highly non-linear. Heuristic algorithms that are frequently employed take a long time and have a chance of missing the optimal microphone array placement design. We extend the Bayesian optimization method to solve the microphone array configuration design problem. The proposed Bayesian optimization method does not depend on gradient and Hessian approximations and makes use of all the information available from prior evaluations. Furthermore, Gaussian process regression and acquisition functions make up the Bayesian optimization method. The objective function is given a prior probabilistic model through Gaussian process regression, which exploits this model while integrating out uncertainty. The acquisition function is adopted to decide the next placement point based upon the incumbent optimum with the posterior distribution. Numerical experiments have demonstrated that the Bayesian optimization method could find a similar or better microphone array placement compared with the hybrid descent method and computational time is significantly reduced. Our proposed method is at least four times faster than the hybrid descent method to find the optimal microphone array configuration from the numerical results. Full article
(This article belongs to the Special Issue Signal Detection and Processing of Sensor Arrays)
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22 pages, 1215 KiB  
Article
Transmit Beamforming Design Based on Multi-Receiver Power Suppression for STAR Digital Array
by Tairan Lin, Xizhang Wei, Jingtong Lai and Mingcong Xie
Sensors 2024, 24(2), 622; https://doi.org/10.3390/s24020622 - 18 Jan 2024
Viewed by 618
Abstract
The simultaneous transmit and receive (STAR) array system provides higher radiation gain and data rate compared to traditional radio system. Because of the various mutual couplings between each pair of transmit and receive elements, it is a great challenge to suppress the incident [...] Read more.
The simultaneous transmit and receive (STAR) array system provides higher radiation gain and data rate compared to traditional radio system. Because of the various mutual couplings between each pair of transmit and receive elements, it is a great challenge to suppress the incident self-interference power at multiple receive elements, which is usually much higher than the desired signal of interest (SoI) power and causes the saturation of receive links and the distortion of the digital SoI. In this paper, we propose an optimized method for transmit beamforming based on radiation power constraints and transmit power control. Through adaptive transmit beamforming, high isolation between the transmit array and each receive link is achieved, minimizing the self-interference power at each receiving element. This method effectively reduces the self-interference power, avoiding distortion of the SoI digital signal caused by limited-bit analog-to-digital converters (ADCs). Simulation results demonstrate that this optimized transmit beamforming method can achieve more than 100 dB effective isotropic isolation (EII) on a 32-element two-dimensional phased array designed in HFSS, reducing the maximum incident self-interference power at the receive channels by approximately 35 dB, while effectively controlling the attenuation of the transmit gain. We also present the advantages in receive subarray isolation and lower ADCs digits under the transmit ABF method. Full article
(This article belongs to the Special Issue Signal Detection and Processing of Sensor Arrays)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Dear Colleagues,

Signal detection and processing of sensor arrays involve the acquisition, analysis, and interpretation of signals from multiple sensors to extract useful information. These processes are commonly used in various fields, including telecommunications, radar systems, medical imaging, industrial and environmental monitoring, smart homes, transportation systems, security systems, and many others. The versatility of sensor arrays, which consist of multiple sensors placed in a specific geometric arrangement, allows for a wide range of applications where extracting useful information from multiple sensors is crucial.  Beamforming is a key technique used in sensor arrays. It involves combining the signals received by different sensors with appropriate weights to form a composite signal. It is also essential to align the acquired signals in time to ensure accurate signal processing. Advanced signal processing methods, such as adaptive algorithms and machine learning, are often employed to improve detection performance and extract valuable information from the sensor array data. The design of optimal algorithms for signal detection, as well as the estimation of the parameters of signals obtained from sensor arrays, are also important. This Special Issue aims to collect high-quality research papers and review articles focusing on a broad range of topics related to sensor arrays and their applications. Potential topics include but are not limited to the following:

  • Beamforming algorithms for sensor arrays
  • Synchronization techniques
  • Asynchronous sensor arrays
  • Source localization and DOA estimation
  • Array calibration and configuration
  • Distributed sensor arrays
  • Artificial Intelligence and Machine Learning techniques for sensor arrays
  • Optimization methods for sensor array configurations
  • Data fusion signal synchronization in sensor arrays
  • Sensor array networks and the IoT
  • Implementation of sensor arrays in embedded systems
  • Novel applications of sensor arrays such as communications, robotic control, automotive, automatic manufacturing, transportation, biomedical
  • Real-time analysis for data in sensor array networks
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