Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
2.9
Journals
Electronics
Special Issues
Advanced Techniques for Radar Signal Processing
share announcement

Advanced Techniques for Radar Signal Processing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 9704

Special Issue Editor

Dr. Peng Chen

E-Mail Website
Guest Editor
State Key Laboratory of Millimeter Waves, Southeast University, Nanjing 210096, China
Interests: radar signal processing; millimeter wave radar system; target localization; super-resolution methods
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Radar systems have the ability to detect, estimate, image, and track targets and are essential in military and civilian applications. Especially with the developments of unmanned aerial vehicles (UAV), millimeter wave technology, artificial intelligence (AI) technology, and intelligent transportation, radar systems require intelligent, efficient, and flexible methods to solve the challenges in practical applications.   

In the last few decades, optimization methods in the radar waveform, the joint radar-communication system, and the resolution allocation methods can significantly improve radar performance and have been studied widely. Additionally, new algorithms for the detection, estimation, imaging, and tracking of targets can enhance efficiency and flexibility. Especially with the development of AI technology, AI-based radar signal processing methods provide new prospects for radar applications. In the array and multiple-input and multiple-output (MIMO) signal processing fields, new direction-finding, beamforming, and interference suppression methods have also been proposed to improve the radar’s overall performance. 

This Special Issue aims to gather the most recent development of radar signal processing, covering, but not limited to, the following scopes:

  • Radar waveform design methods;
  • MIMO radar system design and optimization methods;
  • Target detection, estimation, imaging, and tracking methods;
  • Radar–communication systems;
  • Target localization methods;
  • Resource allocation methods;
  • Array signal processing methods;
  • Interference suppression methods;
  • Artificial-intelligence-based radar signal processing.

Dr. Peng Chen
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 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. Electronics 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 2400 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

  • Waveform design methods
  • MIMO radar applications
  • Radar optimization methods
  • Target detection and estimation methods
  • Target imaging and tracking methods
  • Target localization methods
  • Joint radar–communication systems
  • Resource allocation methods
  • Direction-finding and beamforming methods
  • Interference suppression methods
  • Artificial-intelligence-based radar signal processing

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

12 pages, 489 KiB  
Article
M-ary Phase Position Shift Keying Demodulation Using Stacked Denoising Sparse Autoencoders
by Conghui Lu, Peng Chen, Hua Zhong and Mengyuan Wang
Electronics 2022, 11(8), 1233; https://doi.org/10.3390/electronics11081233 - 14 Apr 2022
Cited by 1 | Viewed by 1184
Abstract
A deep-learning based detector for M-ary phase position shift keying (MPPSK) systems is proposed in this paper. The major components of this detector include a special impact filter, a stacked denoising sparse autoencoder (DSAE), which was trained in unsupervised learning to extract features [...] Read more.
A deep-learning based detector for M-ary phase position shift keying (MPPSK) systems is proposed in this paper. The major components of this detector include a special impact filter, a stacked denoising sparse autoencoder (DSAE), which was trained in unsupervised learning to extract features from the modulation signals, and a softmax classifier. The features learned by the stacked DSAE were then used to train the softmax classifier to demodulate the received signals into M classes. The architecture presented herein was trained and tested on a simple dataset extended by adding Gaussian noise only. The results from the theoretical analysis and simulation show that the detection performance of the proposed scheme is superior to that of existing detectors. Full article
(This article belongs to the Special Issue Advanced Techniques for Radar Signal Processing)
►▼ Show Figures

Figure 1

18 pages, 1065 KiB  
Article
Far-Field DOA Estimation of Uncorrelated RADAR Signals through Coprime Arrays in Low SNR Regime by Implementing Cuckoo Search Algorithm
by Khurram Hameed, Wasim Khan, Yasser S. Abdalla, Fatemah F. Al-Harbi, Ammar Armghan, Muhammad Asif, Muhammad Salman Qamar, Farman Ali, Md Sipon Miah, Mohammad Alibakhshikenari and Mariana Dalarsson
Electronics 2022, 11(4), 558; https://doi.org/10.3390/electronics11040558 - 12 Feb 2022
Cited by 5 | Viewed by 1642
Abstract
For the purpose of attaining a high degree of freedom (DOF) for the direction of arrival (DOA) estimations in radar technology, coprime sensor arrays (CSAs) are evaluated in this paper. In addition, the global and local minima of extremely non-linear functions are investigated, [...] Read more.
For the purpose of attaining a high degree of freedom (DOF) for the direction of arrival (DOA) estimations in radar technology, coprime sensor arrays (CSAs) are evaluated in this paper. In addition, the global and local minima of extremely non-linear functions are investigated, aiming to improve DOF. The optimization features of the cuckoo search (CS) algorithm are utilized for DOA estimation of far-field sources in a low signal-to-noise ratio (SNR) environment. The analytical approach of the proposed CSAs, CS and global and local minima in terms of cumulative distribution function (CDF), fitness function and SNR for DOA accuracy are presented. The parameters like root mean square error (RMSE) for frequency distribution, RMSE variability analysis, estimation accuracy, RMSE for CDF, robustness against snapshots and noise and RMSE for Monte Carlo simulation runs are explored for proposed model performance estimation. In conclusion, the proposed DOA estimation in radar technology through CS and CSA achievements are contrasted with existing tools such as particle swarm optimization (PSO). Full article
(This article belongs to the Special Issue Advanced Techniques for Radar Signal Processing)
►▼ Show Figures

Figure 1

15 pages, 681 KiB  
Article
Reinforcement Learning-Based UAVs Resource Allocation for Integrated Sensing and Communication (ISAC) System
by Min Wang, Peng Chen, Zhenxin Cao and Yun Chen
Electronics 2022, 11(3), 441; https://doi.org/10.3390/electronics11030441 - 1 Feb 2022
Cited by 7 | Viewed by 3050
Abstract
Due to the limited ability of a single unmanned aerial vehicle (UAV), group unmanned aerial vehicles (UAVs) have attracted more attention in communication and radar fields. The use of an integrated sensing and communication (ISAC) system can make communication and radar modules share [...] Read more.
Due to the limited ability of a single unmanned aerial vehicle (UAV), group unmanned aerial vehicles (UAVs) have attracted more attention in communication and radar fields. The use of an integrated sensing and communication (ISAC) system can make communication and radar modules share a radar module’s resources, coupled with efficient resource allocation methods. It can effectively solve the problem of inadequate UAV resources and the low utilization rate of resources. In this paper, the resource allocation problem is addressed for group UAVs to achieve a trade-off between the detection and communication performance, where the ISAC system is equipped in group UAVs. The resource allocation problem is described by an optimization problem, but with group UAVs, the problem is complex and cannot be solved efficiently. Compared with the traditional resource allocation scheme, which needs a lot of calculation or sample set problems, a novel reinforcement-learning-based method is proposed. We formulate a new reward function by combining mutual information (MI) and the communication rate (CR). The MI describes the radar detection performance, and the CR is for wireless communication. Simulation results show that compared with the traditional Kuhn Munkres (KM) or the deep neural network (DNN) methods, this method has better performance with the increase in problem complexity. Additionally, the execution time of this scheme is close to that of the DNN scheme, and it is better than the KM algorithm. Full article
(This article belongs to the Special Issue Advanced Techniques for Radar Signal Processing)
►▼ Show Figures

Figure 1

10 pages, 464 KiB  
Article
TB-NET: A Two-Branch Neural Network for Direction of Arrival Estimation under Model Imperfections
by Liyu Lin, Chaoran She, Yun Chen, Ziyu Guo and Xiaoyang Zeng
Electronics 2022, 11(2), 220; https://doi.org/10.3390/electronics11020220 - 11 Jan 2022
Cited by 2 | Viewed by 1624
Abstract
For direction of arrival (DoA) estimation, the data-driven deep-learning method has an advantage over the model-based methods since it is more robust against model imperfections. Conventionally, networks are based singly on regression or classification and may lead to unstable training and limited resolution. [...] Read more.
For direction of arrival (DoA) estimation, the data-driven deep-learning method has an advantage over the model-based methods since it is more robust against model imperfections. Conventionally, networks are based singly on regression or classification and may lead to unstable training and limited resolution. Alternatively, this paper proposes a two-branch neural network (TB-Net) that combines classification and regression in parallel. The grid-based classification branch is optimized by binary cross-entropy (BCE) loss and provides a mask that indicates the existence of the DoAs at predefined grids. The regression branch refines the DoA estimates by predicting the deviations from the grids. At the output layer, the outputs of the two branches are combined to obtain final DoA estimates. To achieve a lightweight model, only convolutional layers are used in the proposed TB-Net. The simulation results demonstrated that compared with the model-based and existing deep-learning methods, the proposed method can achieve higher DoA estimation accuracy in the presence of model imperfections and only has a size of 1.8 MB. Full article
(This article belongs to the Special Issue Advanced Techniques for Radar Signal Processing)
►▼ Show Figures

Figure 1

8 pages, 4370 KiB  
Article
Microwave Interferometric System for GPR Positioning
by Lapo Miccinesi, Massimiliano Pieraccini and Chiara Lepri
Electronics 2021, 10(22), 2799; https://doi.org/10.3390/electronics10222799 - 15 Nov 2021
Cited by 1 | Viewed by 1302
Abstract
Ground penetrating radar (GPR) systems are sensors that are able to acquire underground images by scanning the surface of the soil/pavement under investigation. Usually, a GPR system records its own position along the scan line, using a mechanical odometer, i.e., a rolling wheel [...] Read more.
Ground penetrating radar (GPR) systems are sensors that are able to acquire underground images by scanning the surface of the soil/pavement under investigation. Usually, a GPR system records its own position along the scan line, using a mechanical odometer, i.e., a rolling wheel in contact with the ground. This simple and cheap solution can be ineffective on uneven terrains. In this paper, a positioning system based on an interferometric radar is presented. This kind of radar is able to detect small displacements of the targets in its field of view. Such a capability was used to track the GPR position along a line. The system was validated with simulations and tested in a realistic experimental scenario. Full article
(This article belongs to the Special Issue Advanced Techniques for Radar Signal Processing)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop