Radar System and Radar Signal Processing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Circuit and Signal Processing".

Deadline for manuscript submissions: 16 December 2024 | Viewed by 2609

Special Issue Editor


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Guest Editor
School of Software Engineering, Xi'an Jiaotong University, Xi'an 710049, China
Interests: machine learning; deep learning; sensors; signal processing

Special Issue Information

Dear Colleagues,

At present, radars play a crucial role in various sectors including defense, meteorology, aviation, space exploration, automobile safety, and human–computer interaction. The aim of this Special Issue is to present the latest research results in the area of radar systems and radar signal processing techniques as a response to the growing demand for radars.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Advances in novel radar systems including Cognitive Radar, MIMO Radar, Quantum Radar, Synthetic Aperture Radar, etc.;
  • Radar signal processing;
  • Algorithms for real-time radar processing;
  • Machine Learning and AI in Radar;
  • Radar applications;
  • Toolboxes for radar signal processing;
  • Novel radar datasets;
  • Literature reviews, benchmarks, and empirical study on radar systems and radar signals.

We look forward to receiving your contributions.

Dr. Fei Wang
Guest Editor

Manuscript Submission Information

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Keywords

  • radar systems
  • radar signal processing
  • radar applications
  • radar algorithms
  • radar dataset
  • radar benchmarks
  • radar toolboxes

Published Papers (3 papers)

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Research

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11 pages, 1875 KiB  
Communication
A Novel Weighted Block Sparse DOA Estimation Based on Signal Subspace under Unknown Mutual Coupling
by Yulong Liu, Yingzeng Yin, Hongmin Lu and Kuan Tong
Electronics 2024, 13(9), 1790; https://doi.org/10.3390/electronics13091790 - 6 May 2024
Viewed by 460
Abstract
In this paper, a novel weighted block sparse method based on the signal subspace is proposed to realize the Direction-of-Arrival (DOA) estimation under unknown mutual coupling in the uniform linear array. Firstly, the signal subspace is obtained by decomposing the eigenvalues of the [...] Read more.
In this paper, a novel weighted block sparse method based on the signal subspace is proposed to realize the Direction-of-Arrival (DOA) estimation under unknown mutual coupling in the uniform linear array. Firstly, the signal subspace is obtained by decomposing the eigenvalues of the sampling covariance matrix. Then, a block sparse model is established based on the deformation of the product of the mutual coupling matrix and the steering vector. Secondly, a suitable set of weighted coefficients is calculated to enhance sparsity. Finally, the optimization problem is transformed into a second-order cone (SOC) problem and solved. Compared with other algorithms, the simulation results of this paper have better performance on DOA accuracy estimation. Full article
(This article belongs to the Special Issue Radar System and Radar Signal Processing)
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20 pages, 12030 KiB  
Article
S2S-Sim: A Benchmark Dataset for Ship Cooperative 3D Object Detection
by Wenbin Yang, Xinzhi Wang, Xiangfeng Luo, Shaorong Xie and Junxi Chen
Electronics 2024, 13(5), 885; https://doi.org/10.3390/electronics13050885 - 26 Feb 2024
Cited by 2 | Viewed by 1019
Abstract
The rapid development of vehicle cooperative 3D object-detection technology has significantly improved the perception capabilities of autonomous driving systems. However, ship cooperative perception technology has received limited research attention compared to autonomous driving, primarily due to the lack of appropriate ship cooperative perception [...] Read more.
The rapid development of vehicle cooperative 3D object-detection technology has significantly improved the perception capabilities of autonomous driving systems. However, ship cooperative perception technology has received limited research attention compared to autonomous driving, primarily due to the lack of appropriate ship cooperative perception datasets. To address this gap, this paper proposes S2S-sim, a novel ship cooperative perception dataset. Ship navigation scenarios were constructed using Unity3D, and accurate ship models were incorporated while simulating sensor parameters of real LiDAR sensors to collect data. The dataset comprises three typical ship navigation scenarios, including ports, islands, and open waters, featuring common ship classes such as container ships, bulk carriers, and cruise ships. It consists of 7000 frames with 96,881 annotated ship bounding boxes. Leveraging this dataset, we assess the performance of mainstream vehicle cooperative perception models when transferred to ship cooperative perception scenes. Furthermore, considering the characteristics of ship navigation data, we propose a regional clustering fusion-based ship cooperative 3D object-detection method. Experimental results demonstrate that our approach achieves state-of-the-art performance in 3D ship object detection, indicating its suitability for ship cooperative perception. Full article
(This article belongs to the Special Issue Radar System and Radar Signal Processing)
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Review

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16 pages, 7524 KiB  
Review
CMOS IC Solutions for the 77 GHz Radar Sensor in Automotive Applications
by Giuseppe Papotto, Alessandro Parisi, Alessandro Finocchiaro, Claudio Nocera, Andrea Cavarra, Alessandro Castorina and Giuseppe Palmisano
Electronics 2024, 13(11), 2104; https://doi.org/10.3390/electronics13112104 - 28 May 2024
Viewed by 453
Abstract
This paper presents recent results on CMOS integrated circuits for automotive radar sensor applications in the 77 GHz frequency band. It is well demonstrated that nano-scale CMOS technologies are the best solution for the implementation of low-cost and high-performance mm-wave radar sensors since [...] Read more.
This paper presents recent results on CMOS integrated circuits for automotive radar sensor applications in the 77 GHz frequency band. It is well demonstrated that nano-scale CMOS technologies are the best solution for the implementation of low-cost and high-performance mm-wave radar sensors since they provide high integration level besides supporting high-speed digital processing. The present work is mainly focused on the RF front-end and summarizes the most stringent requirements of both short/medium- and long-range radar applications. After a brief introduction of the adopted technology, the paper addresses the critical building blocks of the receiver and transmitter chain while discussing crucial design aspects to meet the final performance. Specifically, effective circuit topologies are presented, which concern mixer, variable-gain amplifier, and filter for the receiver, as well as frequency doubler and power amplifier for the transmitter. Moreover, a voltage-controlled oscillator for a PLL efficiently covering the two radar bands is described. Finally, the circuit description is accompanied by experimental results of an integrated implementation in a 28 nm fully depleted silicon-on-insulator CMOS technology. Full article
(This article belongs to the Special Issue Radar System and Radar Signal Processing)
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