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RADAR Sensors and Digital Signal Processing-2nd Edition
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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 3415

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

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Keywords

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

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Published Papers (2 papers)

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Research

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 1568
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)
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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 1398
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)
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