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Applied Sciences
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Recent Progress in Radar Target Detection and Localization
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Recent Progress in Radar Target Detection and Localization

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: 20 January 2025 | Viewed by 3441

Special Issue Editors

Dr. Wanchun Li

E-Mail Website
Guest Editor
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: radar target detection
Dr. Yimao Sun

E-Mail Website
Guest Editor
College of Computer Science, Sichuan University, Chengdu 610065, China
Interests: source localization; parameter estimation; statistical signal processing
Dr. Lin Gao

E-Mail Website
Guest Editor
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: multitarget tracking; information fusion; SLAM; statistical signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Zhongyu Li

E-Mail Website
Guest Editor
The School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: SAR target detection and imaging
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Lu Gan

E-Mail Website
Guest Editor
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: radar beamforming; detection and compressed localization
Dr. Huaguo Zhang

E-Mail Website
Guest Editor
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: statistical signal processing; target tracking; information fusion; array signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Target detection and localization is an area of great importance and research interest in civil and defense radar systems. Recent developments in new technologies, e.g., artificial neural networks, multidimensional data fusion, and new representation models, as well as developments in advanced radar such as MIMO radar and OTH radar, have enhanced the ability to achieve high-performance detection and localization based on radar systems. These techniques have also brought new challenges in designing algorithms for radar detection and localization. The related research topics include multiple-target detection, network resource allocation, sensor placement optimization, data association, target tracking, direct target localization/tracking, distributed processing, and practical experiments. All these developments have motivated the opening of this Special Issue.

With this Special Issue, we aim to collect contributions reporting recent developments in radar detection and localization applications. Topics in the scope of this Special Issue include but are not limited to the following:

  • Detection and/or localization aimed at radar signal processing;
  • Radar detection and localization under complex electromagnetic environments;
  • Radar detection and localization for autopilot and internet of vehicles;
  • Hybrid active/passive networked radar information fusion for target detection and localization;
  • Multiple-target tracking with advanced radar systems;
  • Distributed signal processing over netted radars;
  • Compressive signal processing for radar detection and localization;
  • Robust detection and localization approaches in complex environments;
  • New applications and models of radar detection and localization;
  • High-level radar applications based on the results of detection and localization;
  • Radar source management for detection and localization applications;
  • Artificial intelligence approaches for radar detection and localization.

Dr. Wanchun Li
Dr. Yimao Sun
Dr. Lin Gao
Dr. Zhongyu Li
Prof. Dr. Lu Gan
Dr. Huaguo Zhang
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. Applied Sciences 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

  • source localization
  • intelligent detection
  • resource allocation
  • sensor placement
  • robust localization
  • target tracking

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Published Papers (3 papers)

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Research

17 pages, 7984 KiB  
Article
Design and Application of High-Density Cold Plasma Devices Based on High Curvature Spiked Tungsten Structured Electrodes
by Haotian Weng, Yaozhong Zhang, Xiaolu Huang, Hewei Yuan, Yang Xu, Kun Li, Yunhui Tang and Yafei Zhang
Appl. Sci. 2024, 14(13), 5901; https://doi.org/10.3390/app14135901 - 5 Jul 2024
Viewed by 846
Abstract
Advances in radar technology have driven efforts to develop effective countermeasures. Plasma is recognized as a highly effective medium for absorbing electromagnetic waves. Recent research has focused on enhancing plasma element performance. This paper achieved ultra-high-density, low-pressure cold plasma with a density of [...] Read more.
Advances in radar technology have driven efforts to develop effective countermeasures. Plasma is recognized as a highly effective medium for absorbing electromagnetic waves. Recent research has focused on enhancing plasma element performance. This paper achieved ultra-high-density, low-pressure cold plasma with a density of 1.15 × 1012 cm−3, surpassing similar studies by more than an order of magnitude. Tungsten electrodes with high-curvature spiked structures were invented to replace traditional iron–nickel alloy electrodes, increasing plasma density by 88.2% under the same conditions. Lightweight and cost-effective tubular and annular ultra-high-density, low-pressure cold plasma devices were developed, demonstrating exceptional performance in electromagnetic wave absorption, plasma transient antennas, and radar stealth technology. The influence of plasma on electromagnetic waves and its numerical relationship were analyzed. By measuring the radar cross-section (RCS), the reduction in radar detection rates was quantified. The results show that the ultra-high-density cold plasma devices exhibit very low intrinsic RCS values, suitable for plasma antenna applications. The array of plasma elements generates a large-area high-density low-pressure cold plasma. This plasma effectively reduces the radar cross-section (RCS) of metallic equipment in the S and C bands and shows attenuation in the X band. These effects highlight the superior characteristics of plasma technology in electronic warfare. This exploratory research lays the groundwork for further defense applications. Full article
(This article belongs to the Special Issue Recent Progress in Radar Target Detection and Localization)
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21 pages, 596 KiB  
Article
Enhanced Moving Source Localization with Time and Frequency Difference of Arrival: Motion-Assisted Method for Sub-Dimensional Sensor Networks
by Xu Yang
Appl. Sci. 2024, 14(9), 3909; https://doi.org/10.3390/app14093909 - 3 May 2024
Viewed by 898
Abstract
Localizing a moving source by Time Difference of Arrival (TDOA) and Frequency Difference of Arrival (FDOA) commonly requires at least N+1 sensors in N-dimensional space to obtain more than N pairs of TDOAs and FDOAs, thereby establishing more than [...] Read more.
Localizing a moving source by Time Difference of Arrival (TDOA) and Frequency Difference of Arrival (FDOA) commonly requires at least N+1 sensors in N-dimensional space to obtain more than N pairs of TDOAs and FDOAs, thereby establishing more than 2N equations to solve for 2N unknowns. However, if there are insufficient sensors, the localization problem will become underdetermined, leading to non-unique solutions or inaccuracies in the minimum norm solution. This paper proposes a localization method using TDOAs and FDOAs while incorporating the motion model. The motion between the source and sensors increases the equivalent length of the baseline, thereby improving observability even when using the minimum number of sensors. The problem is formulated as a Maximum Likelihood Estimation (MLE) and solved through Gauss–Newton (GN) iteration. Since GN requires an initialization close to the true value, the MLE is transformed into a semidefinite programming problem using Semidefinite Relaxation (SDR) technology, while SDR results in a suboptimal estimate, it is sufficient as an initialization to guarantee the convergence of GN iteration. The proposed method is analytically shown to reach the Cramér–Rao Lower Bound (CRLB) accuracy under mild noise conditions. Simulation results confirm that it achieves CRLB-level performance when the number of sensors is lower than N+1, thereby corroborating the theoretical analysis. Full article
(This article belongs to the Special Issue Recent Progress in Radar Target Detection and Localization)
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18 pages, 1090 KiB  
Article
Multiple Extended Target Tracking Algorithm Based on Spatio-Temporal Correlation
by Wei Zhang, Chen Lin, Tingting Liu and Lu Gan
Appl. Sci. 2024, 14(6), 2367; https://doi.org/10.3390/app14062367 - 11 Mar 2024
Viewed by 896
Abstract
In the clutter environment, the measurement of a set of multiple extended targets, with an unknown number of targets, poses challenges in partitioning, and the computational cost is high. In particular, the multiple extended target tracking method, based on distance partition, has obvious [...] Read more.
In the clutter environment, the measurement of a set of multiple extended targets, with an unknown number of targets, poses challenges in partitioning, and the computational cost is high. In particular, the multiple extended target tracking method, based on distance partition, has obvious potential estimation errors when the extended targets intersect. This paper proposes a partition algorithm, based on spatio-temporal correlation, which considers the correlation between adjacent moments of the extended target and uses this prior information to divide the measurement set into a survival target measurement set and a born target measurement set for the first time. Then, the survival target measurement set is clustered by the K-means++ algorithm, and the extended target tracking is transformed into point target tracking. The born target measurement undergoes preprocessing by the DBSCAN clustering algorithm, and then uses the directed graph with shared nearest neighbors (SNN) dividing the measurement set. The method proposed in this paper significantly reduces the number of partitions and the computational time. The effectiveness of the algorithm is demonstrated through experimental simulations. Full article
(This article belongs to the Special Issue Recent Progress in Radar Target Detection and Localization)
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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,

Target detection and localization is an area of great importance and research interest in civil and defense radar systems. Recent developments in new technologies, e.g., artificial neural networks, multidimensional data fusion, and new representation models, as well as developments in advanced radar such as MIMO radar and OTH radar, have enhanced the ability to achieve high-performance detection and localization based on radar systems. These techniques have also brought new challenges in designing algorithms for radar detection and localization. The related research topics include multiple-target detection, network resource allocation, sensor placement optimization, data association, target tracking, direct target localization/tracking, distributed processing, and practical experiments. All these developments have motivated the opening of this Special Issue.

With this Special Issue, we aim to collect contributions reporting recent developments in radar detection and localization applications. Topics in the scope of this Special Issue include but are not limited to the following:

  • Detection and/or localization aimed at radar signal processing;
  • Radar detection and localization under complex electromagnetic environments;
  • Radar detection and localization for autopilot and internet of vehicles;
  • Hybrid active/passive networked radar information fusion for target detection and localization;
  • Multiple-target tracking with advanced radar systems;
  • Distributed signal processing over netted radars;
  • Compressive signal processing for radar detection and localization;
  • Robust detection and localization approaches in complex environments;
  • New applications and models of radar detection and localization;
  • High-level radar applications based on the results of detection and localization;
  • Radar source management for detection and localization applications;
  • Artificial intelligence approaches for radar detection and localization.
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