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Eng
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Advances in Signal Processing Techniques and Applications for Radio Systems
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Advances in Signal Processing Techniques and Applications for Radio Systems

A special issue of Eng (ISSN 2673-4117). This special issue belongs to the section "Electrical and Electronic Engineering".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 1217

Editor

Dr. Daniel Filipe Albuquerque

E-Mail Website
Guest Editor
Agueda School of Technology and Management, University of Aveiro, 3750-127 Agueda, Portugal
Interests: location systems; mmWave RADAR; FMCW radar; signal processing; eletronics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Signal processing is crucial for radio systems, enabling reliable information extraction and transmission. Advances in signal processing, driven by the demands of higher data rates and improved robustness, are reshaping radio system design and operation.

This Special Issue, “Advances in Signal Processing Techniques and Applications for Radio Systems”, aims to present state-of-the-art research covering both theoretical advances and practical implementations of signal processing in radio-based systems. It seeks to provide a multidisciplinary forum addressing the current challenges and emerging applications across communication, sensing, and monitoring domains.

Topics of interest include, but are not limited to, the following:

  1. Advanced Signal Processing for Radio Communications:
    • Modulation, demodulation, and waveform design.
    • Channel estimation, equalization, and coding techniques.
    • MIMO systems, beamforming, and massive antenna arrays.
    • Interference mitigation and coexistence strategies.
  2. Radio System Architectures and RF Signal Processing:
    • RF front-end modeling, calibration, and compensation.
    • Spectrum sensing, management, and sharing.
    • Software-defined radio (SDR) and reconfigurable platforms.
    • Hardware–software co-design for radio systems.
  3. Emerging Wireless Technologies and Applications:
    • 5G, 6G, and beyond wireless systems.
    • Internet of Things (IoT) and machine-to-machine communications.
    • Cognitive and intelligent radio systems.
    • Joint communication and sensing (JCAS / ISAC).
  4. Radar, Sensing, and Navigation Signal Processing:
    • Radar signal processing and target detection/tracking.
    • Imaging radar, SAR, and inverse SAR techniques.
    • Localization, positioning, and navigation algorithms.
    • Passive, multistatic, and distributed radar systems.
  5. Data-Driven and Intelligent Signal Processing:
    • Machine learning and artificial intelligence for radio systems.
    • Adaptive and self-learning signal processing techniques.
    • Sparse signal processing and compressive sensing.
    • Anomaly detection and signal classification.

Dr. Daniel Filipe Albuquerque
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 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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Eng is an international peer-reviewed open access monthly 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 1400 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

  • signal processing
  • radio systems
  • wireless communications
  • RF signal processing
  • modulation and waveform design
  • channel estimation and equalization
  • MIMO and beamforming
  • spectrum sensing and management
  • cognitive radio
  • software-defined radio (SDR)
  • radar signal processing
  • target detection and tracking
  • radar imaging (SAR/ISAR)
  • localization and navigation
  • joint communication and sensing (ISAC)
  • adaptive filtering
  • machine learning for radio systems
  • sparse signal processing
  • compressive sensing
  • interference mitigation

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

26 pages, 15001 KB  
Article
An IoT-Enabled LoRa Communication-Based Hydrogen Leak Localization System Using Machine Learning
by Arif Ibrahim and József Sárosi
Eng 2026, 7(8), 421; https://doi.org/10.3390/eng7080421 - 19 Aug 2026
Viewed by 167
Abstract
Hydrogen leakage detection and mapping are essential in hydrogen-rich environments to ensure safe utilization in industrial and commercial applications. In this study, a wireless IoT-enabled hydrogen leak-mapping system was developed using machine learning and a LoRa-coupled wireless sensor network. A miniature model of [...] Read more.
Hydrogen leakage detection and mapping are essential in hydrogen-rich environments to ensure safe utilization in industrial and commercial applications. In this study, a wireless IoT-enabled hydrogen leak-mapping system was developed using machine learning and a LoRa-coupled wireless sensor network. A miniature model of a hydrogen production system was used, featuring a functioning electrolyzer that generates pure hydrogen by splitting water. To perform efficient leakage mapping, the leak location and watch time were varied, and six readings from commercial hydrogen gas sensors were recorded for better analysis. The relative sensor responses recorded by the six hydrogen sensors were used as input features for the machine learning models. The model accuracy was approximately 88.13%. LoRa communication technology was also used to demonstrate its use in harsh conditions, along with the IoT protocol, to deliver data over the Internet for better accessibility and monitoring. The developed localization technology enables safe monitoring of hazardous, highly flammable hydrogen gas, and machine learning can help prevent fatal accidents. Full article
(This article belongs to the Special Issue Advances in Signal Processing Techniques and Applications for Radio Systems)
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19 pages, 3073 KB  
Article
An Effective Reduced-Dimension EPC-STAP for Limited Snapshots Under Range Ambiguity
by Yue Zhao, Zhao Wang, Xuecong Li, Chao Xu, Di Song and Jinmin Shi
Eng 2026, 7(8), 367; https://doi.org/10.3390/eng7080367 - 25 Jul 2026
Viewed by 248
Abstract
Space-time adaptive processing (STAP) is regarded as a highly effective approach for clutter mitigation in airborne radar applications. However, in practical range-ambiguous scenarios, the clutter suppression performance of traditional STAP algorithms can be severely degraded. Element-pulse coding (EPC) radar introduces extra controllable freedoms [...] Read more.
Space-time adaptive processing (STAP) is regarded as a highly effective approach for clutter mitigation in airborne radar applications. However, in practical range-ambiguous scenarios, the clutter suppression performance of traditional STAP algorithms can be severely degraded. Element-pulse coding (EPC) radar introduces extra controllable freedoms by assigning frequency offsets among array elements, which provides a promising means to cope with the influence of range ambiguity. On this basis, EPC-assisted STAP techniques have attracted increasing attention. Even so, the large number of adaptive degrees of freedom (DoFs) required by EPC-STAP makes its performance highly dependent on sufficient training snapshots, and a noticeable degradation may occur when only limited snapshots are available. To overcome this limitation, this study focuses on reduced-dimension (RD) STAP for airborne EPC radar under range-ambiguous conditions and develops an efficient reduced-dimension EPC-STAP scheme. Specifically, the received signal model of the airborne EPC-STAP system is first formulated to characterize the data structure. Subsequently, a tailored linear mapping matrix is constructed to compress the original high-dimensional observation space. The resulting RD-EPC-STAP processor is then obtained according to the minimum variance distortionless response principle using the transformed lower-dimensional data. Numerical experiments verify that the proposed algorithm provides improved clutter rejection capability while retaining strong tolerance to array gain and phase errors. Full article
(This article belongs to the Special Issue Advances in Signal Processing Techniques and Applications for Radio Systems)
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17 pages, 17486 KB  
Article
Robust FDA-STAP Under Transmit Array Gain and Phase Error for Range Ambiguity
by Di Song, Chunyu Song, Fangyu Wang and Jinmin Shi
Eng 2026, 7(7), 327; https://doi.org/10.3390/eng7070327 - 6 Jul 2026
Viewed by 260
Abstract
Frequency diverse array (FDA)-aided spacetime adaptive processing (STAP) is a useful approach to suppressing range-ambiguous clutter in airborne radars. In general, FDA-STAP schemes based on subspace techniques can provide excellent clutter rejection capability when only limited secondary data are available. However, transmit array [...] Read more.
Frequency diverse array (FDA)-aided spacetime adaptive processing (STAP) is a useful approach to suppressing range-ambiguous clutter in airborne radars. In general, FDA-STAP schemes based on subspace techniques can provide excellent clutter rejection capability when only limited secondary data are available. However, transmit array gain and phase errors may cause serious performance degradation. To overcome this problem, a robust subspace-based FDA-STAP algorithm is proposed in this work: RSUB-FDA-STAP. Firstly, the influence of transmit array errors on subspace-based FDA-STAP is analyzed, indicating that the errors must be calibrated. Secondly, an error calibration vector is constructed, and an optimization problem is formulated to estimate the errors. Finally, by minimizing the objective function of the optimization problem, the errors are effectively estimated with limited secondary data, and an effective RSUB-FDA-STAP is developed for range-ambiguous clutter suppression. The proposed method effectively suppresses range-ambiguous clutter under transmit array gain and phase errors in comparison with existing robust FDA-STAP techniques. Numerical simulations demonstrate that the developed RSUB-FDA-STAP shows superior robustness against transmit array gain and phase errors. Full article
(This article belongs to the Special Issue Advances in Signal Processing Techniques and Applications for Radio Systems)
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17 pages, 4614 KB  
Article
Reduced-Dimension FDA-STAP for Range-Ambiguous Clutter Rejection Under Limited Snapshots
by Di Song, Chunyu Song, Qiqi Li and Jinmin Shi
Eng 2026, 7(7), 324; https://doi.org/10.3390/eng7070324 - 2 Jul 2026
Viewed by 226
Abstract
Space-time adaptive processing (STAP) has been widely recognized as an effective technique for suppressing clutter in airborne radar systems. Nevertheless, range ambiguity can seriously affect the clutter rejection capability of conventional STAP methods. Frequency diverse array (FDA) radar provides additional degrees of freedom [...] Read more.
Space-time adaptive processing (STAP) has been widely recognized as an effective technique for suppressing clutter in airborne radar systems. Nevertheless, range ambiguity can seriously affect the clutter rejection capability of conventional STAP methods. Frequency diverse array (FDA) radar provides additional degrees of freedom by exploiting frequency offsets across array elements, thereby offering an effective solution to the range ambiguity problem. Based on this advantage, FDA-STAP has been proposed and developed. However, when the available snapshots are insufficient, the performance of FDA-STAP may deteriorate significantly due to the high-dimensional adaptive processing requirement. To address this issue, this paper investigates reduced-dimension STAP for airborne FDA radar in range-ambiguous environments and proposes an enhanced reduced-dimension FDA-STAP method, referred to as ERD-FDA-STAP. First, the signal model of airborne FDA-STAP radar is established to describe the received data characteristics. Then, a specially designed linear transformation matrix is introduced to decrease the system degrees of freedom. Finally, the proposed ERD-FDA-STAP algorithm is derived based on the minimum variance distortionless response criterion using the reduced-dimension data. Simulation results demonstrate that the proposed method achieves superior clutter suppression performance and maintains good robustness against array gain and phase errors. Full article
(This article belongs to the Special Issue Advances in Signal Processing Techniques and Applications for Radio Systems)
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