Innovations in AI and Signal Processing for Advanced Sensing, Radar, RFID, and Communication Systems

Topic Information

Dear Colleagues,

We are pleased to invite contributions to this Special Issue focusing on Innovations in AI and Signal Processing for Advanced Sensing, Radar, RFID, and Communication Systems. This edition aims to explore the transformative role of Artificial Intelligence (AI), Machine Learning (ML), and advanced signal processing techniques in shaping next-generation sensing and communication technologies.

This Topic aims to highlight innovative research and practical applications that address critical challenges and unlock new opportunities in a range of interconnected domains, including:

• Advanced Sensing Technologies: Exploring AI/ML-driven advancements in sensors for diverse applications.
• Radar Systems: Investigating signal processing and AI techniques for enhancing radar accuracy, resolution, and adaptability.
• RFID Systems: Examining how intelligent algorithms and signal processing can optimize RFID performance in industrial, agricultural, and healthcare applications.
• Communication Technologies: Showcasing breakthroughs in antenna design and signal processing for 5G and beyond.
• Emerging Applications: Delving into AI/ML’s role in areas such as Brain–Computer Interfaces (BCI) and biometric authentication.

This Special Issue seeks to provide a comprehensive platform for researchers and practitioners to present their cutting-edge findings, share insights, and discuss challenges in these rapidly evolving fields. Contributions may include original research articles, case studies, and reviews, focusing on theoretical advancements, practical implementations, or experimental studies.

We look forward to receiving your valuable contributions.

Dr. Shuvashis Dey
Dr. Dipankar Mitra
Dr. Rig Das
Dr. Giovanni Andrea Casula
Prof. Dr. Francisco Falcone
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI ai	3.1	7.2	2020	18.9 Days	CHF 1600	Submit
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400	Submit
Electronics electronics	2.6	5.3	2012	16.4 Days	CHF 2400	Submit
Sensors sensors	3.4	7.3	2001	18.6 Days	CHF 2600	Submit
Signals signals	-	3.2	2020	28.3 Days	CHF 1000	Submit
Telecom telecom	2.1	4.8	2020	20.5 Days	CHF 1200	Submit
Technologies technologies	4.2	6.7	2013	21.1 Days	CHF 1600	Submit

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Published Papers (3 papers)

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14 pages, 1361 KiB  

Open AccessArticle

Multiple Targets CFAR Detection Performance Based on an Intelligent Clustering Algorithm in K-Distribution Sea Clutter

by Mansoor M. Al-dabaa, Eugen Laslo, Ahmed A. Emran, Ahmed Yahya and Ashraf Aboshosha

Sensors 2025, 25(8), 2613; https://doi.org/10.3390/s25082613 - 20 Apr 2025

Viewed by 168

Abstract

Maintaining a Constant False Alarm Rate (CFAR) in the presence of K-distributed sea clutter is vital due to the dynamic and unpredictable nature of maritime environments. However, conventional CFAR detectors suffer significant performance degradation in multi-target scenarios, primarily due to the masking effect [...] Read more.

Maintaining a Constant False Alarm Rate (CFAR) in the presence of K-distributed sea clutter is vital due to the dynamic and unpredictable nature of maritime environments. However, conventional CFAR detectors suffer significant performance degradation in multi-target scenarios, primarily due to the masking effect caused by interfering targets. To address this challenge, this paper introduces an advanced detection scheme that integrates Linear Density-Based Spatial Clustering for Applications with Noise (Lin-DBSCAN) with CFAR processing. Lin-DBSCAN is specifically tailored to efficiently identify and isolate interfering targets and sea spikes, which typically manifest as outliers in the symmetric reference windows surrounding the Cell Under Test (CUT). By leveraging Lin-DBSCAN, the proposed Lin-DBSCAN-CFAR method effectively filters out anomalous signals from the background clutter, resulting in enhanced detection accuracy and robustness, especially under complex sea clutter conditions. Extensive simulations under varying conditions, including multiple target environments, varying false alarm rates, and different clutter shape parameters, demonstrate that Lin-DBSCAN-CFAR significantly outperforms conventional CFAR approaches. It is noteworthy that the proposed method achieves detection performance comparable to the more computationally intensive DBSCAN-CFAR while significantly reducing computational complexity. Simulation results reveal that Lin-DBSCAN-CFAR requires a 1 to 2 dB lower SNR to reach a detection probability of 0.8 compared with the nearest traditional CFAR techniques, confirming its superiority in both accuracy and efficiency. Full article

(This article belongs to the Topic Innovations in AI and Signal Processing for Advanced Sensing, Radar, RFID, and Communication Systems)

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17 pages, 17326 KiB  

Open AccessArticle

Enhanced Adaptive Sine Multi-Taper Power Spectral Density Estimation for System Performance Evaluation in Low-Frequency Gravitational Wave Detection

by Caiyun Liu, Yang Li, Changkang Fu, Hongming Zhang, Qiang Wang, Dong He and Yongmei Huang

Appl. Sci. 2025, 15(7), 3919; https://doi.org/10.3390/app15073919 - 3 Apr 2025

Viewed by 209

Abstract

The power spectral density estimation algorithms, logarithmic frequency axis for power spectral density (LPSD), and the LISA-LPSD algorithm are widely utilized in the implementation of system evaluations for space-based gravitational-wave-detection projects, particularly in the low-frequency band ranging from 0.1 mHz to 1 Hz. [...] Read more.

The power spectral density estimation algorithms, logarithmic frequency axis for power spectral density (LPSD), and the LISA-LPSD algorithm are widely utilized in the implementation of system evaluations for space-based gravitational-wave-detection projects, particularly in the low-frequency band ranging from 0.1 mHz to 1 Hz. However, existing adaptive sine multi-taper algorithms suffer from low resolution and high computational complexity in obtaining the optimal cone number across the entire frequency domain, which has hindered its application in this field. These algorithms often face challenges related to inadequate resolution when dealing with low-frequency signals, as well as the issue of high computational demands. In response to these challenges, this paper introduces an advanced adaptive sine multi-taper algorithm designed to optimize the determination of the cone number. By balancing the relationship between bias and variance, this approach facilitates gradient processing of the cone number specifically tailored for low-frequency signals. Comparative evaluations against the LPSD algorithm, the original adaptive sine multi-taper algorithm, and the LISA-LPSD algorithm reveal that the proposed method demonstrates superior spectral resolution and reduced algorithmic complexity. This improvement offers a more effective solution for the system evaluation of low-frequency gravitational-wave-detection projects. Full article

(This article belongs to the Topic Innovations in AI and Signal Processing for Advanced Sensing, Radar, RFID, and Communication Systems)

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25 pages, 7385 KiB  

Open AccessArticle

Integrated Waveform Design and Signal Processing Based on Composite Noise Nimble Modulated Signals

by Xinquan Cao, Shiyuan Zhang, Ke Tan, Xingyu Lu, Jianchao Yang, Zheng Dai and Hong Gu

Electronics 2025, 14(6), 1227; https://doi.org/10.3390/electronics14061227 - 20 Mar 2025

Viewed by 199

Abstract

In modern radar operations, detection and jamming systems play a critical role. Integrated detection and jamming systems simultaneously fulfill both functions, thereby optimizing resource utilization. In this paper, we introduce a novel random noise frequency modulation nimble modulation integrated signal (RNFM-NMIS) that is [...] Read more.

In modern radar operations, detection and jamming systems play a critical role. Integrated detection and jamming systems simultaneously fulfill both functions, thereby optimizing resource utilization. In this paper, we introduce a novel random noise frequency modulation nimble modulation integrated signal (RNFM-NMIS) that is designed based on reconnaissance analysis of adversary linear frequency modulated (LFM) radar signal parameters. This waveform facilitates flexible adjustment of parameters, enabling adaptive detection and jamming functions. Furthermore, to address the challenge of direct-wave interference from adversary transmissions, we propose a signal processing method based on time-domain pre-cancellation (TDPC). Simulation and experimental results show that the proposed integrated waveform exhibits excellent and adjustable detection and jamming capabilities. Under the proposed processing method, interference suppression and target detection performance are significantly enhanced, achieving substantial improvements over traditional methods. Full article

(This article belongs to the Topic Innovations in AI and Signal Processing for Advanced Sensing, Radar, RFID, and Communication Systems)

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