Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.9
Journals
Sensors
Special Issues
RADAR Sensors and Digital Signal Processing-2nd Edition
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

RADAR Sensors and Digital Signal Processing-2nd Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Radar Sensors".

Deadline for manuscript submissions: closed (20 February 2024) | Viewed by 3187

Special Issue Editor

Prof. Dr. Seongjoo Lee

E-Mail Website
Guest Editor
1. Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea
2. Department of Convergence Engineering of Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea
Interests: RADAR; LiDAR; DSP; IoT; motion recognition; deep learning; machine learning; SoC
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This publication is a continuation of our previous Special Issue on the same topic, entitled “RADAR Sensors and Digital Signal Processing”.

RADAR and LiDAR were originally developed for military purposes. However, they now represent cutting-edge technologies that are widely used in commercial products. Although many studies into RADAR and LiDAR sensors focus on analog design, digital signal processing to improve the performance of RADAR and LiDAR sensors is also a very important area of study. Intensive research is also required for many application services using RADAR and LiDAR sensors.

This Special Issue is addressed to all types of DSP and any applications of RADAR and LiDAR sensors.

Prof. Dr. Seongjoo Lee
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. Sensors 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 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

  • RADAR sensors
  • LiDAR sensors
  • digital signal processing for RADAR sensors
  • digital signal processing for LiDAR sensors
  • RADAR sensor applications
  • LiDAR sensor applications

Published Papers (3 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

23 pages, 12815 KiB  
Article
Hand Gesture Recognition Using FSK Radar Sensors
by Kimoon Yang, Minji Kim, Yunho Jung and Seongjoo Lee
Sensors 2024, 24(2), 349; https://doi.org/10.3390/s24020349 - 06 Jan 2024
Viewed by 811
Abstract
Hand gesture recognition, which is one of the fields of human–computer interaction (HCI) research, extracts the user’s pattern using sensors. Radio detection and ranging (RADAR) sensors are robust under severe environments and convenient to use for hand gestures. The existing studies mostly adopted [...] Read more.
Hand gesture recognition, which is one of the fields of human–computer interaction (HCI) research, extracts the user’s pattern using sensors. Radio detection and ranging (RADAR) sensors are robust under severe environments and convenient to use for hand gestures. The existing studies mostly adopted continuous-wave (CW) radar, which only shows a good performance at a fixed distance, which is due to its limitation of not seeing the distance. This paper proposes a hand gesture recognition system that utilizes frequency-shift keying (FSK) radar, allowing for a recognition method that can work at the various distances between a radar sensor and a user. The proposed system adopts a convolutional neural network (CNN) model for the recognition. From the experimental results, the proposed recognition system covers the range from 30 cm to 180 cm and shows an accuracy of 93.67% over the entire range. Full article
(This article belongs to the Special Issue RADAR Sensors and Digital Signal Processing-2nd Edition)
►▼ Show Figures

Figure 1

12 pages, 5100 KiB  
Communication
FMCW Radar Sensors with Improved Range Precision by Reusing the Neural Network
by Homin Cho, Yunho Jung and Seongjoo Lee
Sensors 2024, 24(1), 136; https://doi.org/10.3390/s24010136 - 26 Dec 2023
Viewed by 914
Abstract
This paper addresses the challenge of enhancing range precision in radar sensors through supervised learning. However, when the range precision surpasses the range resolution, it leads to a rapid increase in the number of labels, resulting in elevated learning costs. The removal of [...] Read more.
This paper addresses the challenge of enhancing range precision in radar sensors through supervised learning. However, when the range precision surpasses the range resolution, it leads to a rapid increase in the number of labels, resulting in elevated learning costs. The removal of background noise in indoor environments is also crucial. In response, this study proposes a methodology aiming to increase range precision while mitigating the issue of a growing number of labels in supervised learning. Neural networks learned for a specific section are reused to minimize learning costs and maximize computational efficiency. Formulas and experiments confirmed that identical fractional multiple patterns in the frequency domain can be applied to analyze patterns in other FFT bin positions (representing different target positions). In conclusion, the results suggest that neural networks trained with the same data can be repurposed, enabling efficient hardware implementation. Full article
(This article belongs to the Special Issue RADAR Sensors and Digital Signal Processing-2nd Edition)
►▼ Show Figures

Figure 1

10 pages, 1309 KiB  
Communication
IDBD-Based Beamforming Algorithm for Improving the Performance of Phased Array Radar in Nonstationary Environments
by Shihan Wang, Tao Chen and Hongjian Wang
Sensors 2023, 23(6), 3211; https://doi.org/10.3390/s23063211 - 17 Mar 2023
Viewed by 1098
Abstract
Adaptive array processing technology for a phased array radar is usually based on the assumption of a stationary environment; however, in real-world scenarios, nonstationary interference and noise deteriorate the performance of the traditional gradient descent algorithm, in which the learning rate of the [...] Read more.
Adaptive array processing technology for a phased array radar is usually based on the assumption of a stationary environment; however, in real-world scenarios, nonstationary interference and noise deteriorate the performance of the traditional gradient descent algorithm, in which the learning rate of the tap weights is fixed, leading to errors in the beam pattern and a reduced output signal-to-noise ratio (SNR). In this paper, we use the incremental delta-bar-delta (IDBD) algorithm, which has been widely used for system identification problems in nonstationary environments, to control the time-varying learning rates of the tap weights. The designed iteration formula for the learning rate ensures that the tap weights adaptively track the Wiener solution. The results of numerical simulations show that in a nonstationary environment, the traditional gradient descent algorithm with a fixed learning rate has a distorted beam pattern and reduced output SNR; however, the IDBD-based beamforming algorithm, in which a secondary control mechanism is used to adaptively update the learning rates, showed a similar beam pattern and output SNR to a traditional beamformer in a Gaussian white noise background; that is, the main beam and null satisfied the pointing constraints, and the optimal output SNR was obtained. Although the proposed algorithm contains a matrix inversion operation, which has considerable computational complexity, this operation could be replaced by the Levinson–Durbin iteration due to the Toeplitz characteristic of the matrix; therefore, the computational complexity could be decreased to O(n), so additional computing resources are not required. Moreover, according to some intuitive interpretations, the reliability and stability of the algorithm are guaranteed. Full article
(This article belongs to the Special Issue RADAR Sensors and Digital Signal Processing-2nd Edition)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop