AI Based Signal Processing for Drones

A special issue of Drones (ISSN 2504-446X).

Deadline for manuscript submissions: closed (14 July 2023) | Viewed by 3442

Special Issue Editor


E-Mail Website
Guest Editor
Department of Embedded Systems Engineering, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon 22012, Republic of Korea
Interests: remote sensing; deep learning; artificial intelligence; image processing; signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

AI-based signal processing for drones represents a huge development in the topic of drone vision and in solving the challenges of that field (especially from an image/video analysis and computer vision point of view). The demand for drones is swiftly increasing. Drone vision plays an important role in perception and control, improving safety by localization and mapping, problem detection and landing detection. In addition, drone vision can accomplish visual data gaining, especially in media production applications. To achieve this ability, the drone must possess qualities of augmented multiple drone decisional autonomy and enhanced multiple drone robustness mechanisms. To this end, communication safety, embedded flight regulation compliance, enhanced crowd avoidance and emergency landing mechanisms are vital factors. Drone vision and deep leaning learning can be solid tools for the attainment of this goal. This Special Issue calls for recent studies on various AI-based signal processing methods for drones. Papers of both theoretical and applicative natures are welcome, as are contributions regarding new signal processing techniques for use in the drone research community. In this Special Issue, we will compile state-of-the-art research that addresses various aspects of AI-based signal processing for drones.

Potential topics include, but are not limited to, the following areas: new AI concepts, ideas, and technologies of signal processing for drones; evaluation of current advanced signal processing methods for drones; autonomous maneuvers, supported by AI; signal processing to reduce drone’s noise emissions; AI-based signal processing for drone tracking, challenges, and applications; AI-based signal processing for drone signature detection or suppression; semantic world mapping; multiple drone and multiple target localization; drone visual analysis for target/obstacle/crowd/point of interest detection; 2D/3D target tracking.

Dr. Gwanggil Jeon
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. Drones 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 2600 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

  • drones
  • deep learning
  • signal processing
  • image processing
  • video processing
  • AI

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

18 pages, 1101 KiB  
Article
Self-Supervised Representation Learning for Quasi-Simultaneous Arrival Signal Identification Based on Reconnaissance Drones
by Linqing Guo, Mingyang Du, Jingwei Xiong, Zilong Wu and Jifei Pan
Drones 2023, 7(7), 475; https://doi.org/10.3390/drones7070475 - 19 Jul 2023
Cited by 1 | Viewed by 1113
Abstract
Reconnaissance unmanned aerial vehicles are specifically designed to estimate parameters and process intercepted signals for the purpose of identifying and locating radars. However, distinguishing quasi-simultaneous arrival signals (QSAS) has become increasingly challenging in complex electromagnetic environments. In order to address the problem, a [...] Read more.
Reconnaissance unmanned aerial vehicles are specifically designed to estimate parameters and process intercepted signals for the purpose of identifying and locating radars. However, distinguishing quasi-simultaneous arrival signals (QSAS) has become increasingly challenging in complex electromagnetic environments. In order to address the problem, a framework for self-supervised deep representation learning is proposed. The framework consists of two phases: (1) pre-train an autoencoder. For learning the unlabeled QSAS representation, the ConvNeXt V2 is trained to extract features from masked time–frequency images and reconstruct the corresponding signal in both time and frequency domains; (2) transfer the learned knowledge. For downstream tasks, encoder layers are frozen, the linear layer is fine-tuned to classify QSAS under few-shot conditions. Experimental results demonstrate that the proposed algorithm can achieve an average recognition accuracy of over 81% with the signal-to-noise ratio in the range of −16∼16 dB. Compared to existing CNN-based and Transformer-based neural networks, the proposed algorithm shortens the time of testing by about 11× and improves accuracy by up to 21.95%. Full article
(This article belongs to the Special Issue AI Based Signal Processing for Drones)
Show Figures

Figure 1

21 pages, 2868 KiB  
Article
IRelNet: An Improved Relation Network for Few-Shot Radar Emitter Identification
by Zilong Wu, Meng Du, Daping Bi and Jifei Pan
Drones 2023, 7(5), 312; https://doi.org/10.3390/drones7050312 - 8 May 2023
Cited by 3 | Viewed by 1406
Abstract
In future electronic warfare (EW), there will be many unmanned aerial vehicles (UAVs) equipped with electronic support measure (ESM) systems, which will often encounter the challenge of radar emitter identification (REI) with few labeled samples. To address this issue, we propose a novel [...] Read more.
In future electronic warfare (EW), there will be many unmanned aerial vehicles (UAVs) equipped with electronic support measure (ESM) systems, which will often encounter the challenge of radar emitter identification (REI) with few labeled samples. To address this issue, we propose a novel deep learning network, IRelNet, which could be easily embedded in the computer system of a UAV. This network was designed with channel attention, spatial attention and skip-connect features, and meta-learning technology was applied to solve the REI problem. IRelNet was trained using simulated radar emitter signals and can effectively extract the essential features of samples in a new task, allowing it to accurately predict the class of the emitter to be identified. Furthermore, this work provides a detailed description of how IRelNet embedded in a UAV was applied in the EW scene and verified its effectiveness via experiments. When the signal-to-noise ratio (SNR) was 4 dB, IRelNet achieved an identification accuracy of greater than 90% on the samples in the test task. Full article
(This article belongs to the Special Issue AI Based Signal Processing for Drones)
Show Figures

Figure 1

Back to TopTop