scholarly journals Artificial Neural Networks and Deep Learning Techniques Applied to Radar Target Detection: A Review

Electronics ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 156
Author(s):  
Wen Jiang ◽  
Yihui Ren ◽  
Ying Liu ◽  
Jiaxu Leng
Keyword(s):  
Signal Processing ◽  
Deep Learning ◽  
Target Detection ◽  
Radar Signal ◽  
Radar Signal Processing ◽  
Radar Target ◽  
Future Studies ◽  
Learning Techniques ◽  
Feasible Solutions ◽  
Novel Concept

Radar target detection (RTD) is a fundamental but important process of the radar system, which is designed to differentiate and measure targets from a complex background. Deep learning methods have gained great attention currently and have turned out to be feasible solutions in radar signal processing. Compared with the conventional RTD methods, deep learning-based methods can extract features automatically and yield more accurate results. Applying deep learning to RTD is considered as a novel concept. In this paper, we review the applications of deep learning in the field of RTD and summarize the possible limitations. This work is timely due to the increasing number of research works published in recent years. We hope that this survey will provide guidelines for future studies and applications of deep learning in RTD and related areas of radar signal processing.

Machine Learning and Deep Learning Techniques for Colocated MIMO Radars: A Tutorial Overview

2021 ◽  
Author(s):  
ALESSANDRO DAVOLI ◽  
Giorgio Guerzoni ◽  
Giorgio Matteo Vitetta
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Deep Learning ◽  
Antenna Arrays ◽  
Research Work ◽  
Radar Signal ◽  
Open Problems ◽  
Technical Problems ◽  
Radar Systems ◽  
Learning Techniques

<p>Radars are expected to become the main sensors in various civilian applications, ranging from health-care monitoring to autonomous driving. Their success is mainly due to the availability of both low cost integrated devices, equipped with compact antenna arrays, and computationally efficient signal processing techniques. An increasingly important role in the field of radar signal processing is played by machine learning and deep learning techniques. Their use has been first taken into consideration in human gesture and motion recognition, and in various healthcare applications. More recently, their exploitation in object detection and localization has been also investigated. The research work accomplished in these areas has raised various technical problems that need to be carefully addressed before adopting the above mentioned techniques in real world radar systems. In this manuscript, a comprehensive overview of the machine learning and deep learning techniques currently being considered for their use in radar systems is provided. Moreover, some relevant open problems and current trends in this research area are analysed. Finally, various numerical results, based on both synthetically generated and experimental datasets, and referring to two different applications are illustrated. These allow readers to assess the efficacy of specific methods and to compare them in terms of accuracy and computational effort.</p>

scholarly journals Machine Learning and Deep Learning Techniques for Colocated MIMO Radars: A Tutorial Overview

2021 ◽  
Author(s):  
ALESSANDRO DAVOLI ◽  
Giorgio Guerzoni ◽  
Giorgio Matteo Vitetta
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Deep Learning ◽  
Antenna Arrays ◽  
Research Work ◽  
Radar Signal ◽  
Open Problems ◽  
Technical Problems ◽  
Radar Systems ◽  
Learning Techniques

<p>Radars are expected to become the main sensors in various civilian applications, ranging from health-care monitoring to autonomous driving. Their success is mainly due to the availability of both low cost integrated devices, equipped with compact antenna arrays, and computationally efficient signal processing techniques. An increasingly important role in the field of radar signal processing is played by machine learning and deep learning techniques. Their use has been first taken into consideration in human gesture and motion recognition, and in various healthcare applications. More recently, their exploitation in object detection and localization has been also investigated. The research work accomplished in these areas has raised various technical problems that need to be carefully addressed before adopting the above mentioned techniques in real world radar systems. In this manuscript, a comprehensive overview of the machine learning and deep learning techniques currently being considered for their use in radar systems is provided. Moreover, some relevant open problems and current trends in this research area are analysed. Finally, various numerical results, based on both synthetically generated and experimental datasets, and referring to two different applications are illustrated. These allow readers to assess the efficacy of specific methods and to compare them in terms of accuracy and computational effort.</p>

scholarly journals Comparison of the performance of adaptive space-time processing against the background of alternative methods

2020 ◽  
Vol 69 (2) ◽  
pp. 129-147
Author(s):  
Anna Ślesicka ◽  
Adam Kawalec
Keyword(s):  
Signal Processing ◽  
Antenna Array ◽  
Target Detection ◽  
Space Time ◽  
Alternative Methods ◽  
Radar Signal ◽  
Radar Signal Processing ◽  
Time Processing ◽  
Simulation Results ◽  
Space Time Adaptive Processing

Description and successive stages of the STAP algorithm were characterized in this article. The ability to detect an object by using 6-element antenna array without space-time processing and using the STAP technique were compared and shown. The simulation results showed that the implemented STAP algorithm successfully coped with target detection. In addition, the possibilities of object detection using the STAP technique were compared and shown against the background of other DPCA and ADPCA algorithms. Keywords: space-time adaptive processing, STAP, DPCA, ADPCA, radar signal processing, radar

RADAR TARGET IMITATOR

2019 ◽  
Vol 149 (1) ◽  
pp. 75-83
Author(s):  
Stanisław Żygadło ◽  
Stanisław Grzywiński ◽  
Krzysztof ACHTENBERG
Keyword(s):  
Signal Processing ◽  
Frequency Band ◽  
Single Channel ◽  
Intermediate Frequency ◽  
Radar Signal ◽  
Radar Signal Processing ◽  
Radar Target ◽  
Analog Channel ◽  
Radar Signals

The paper describes a single channel imitator for radar signals which was designed and made practically to generate a signal reflected from an aerial target with a given trajectory on the intermediate frequency band. Presented setup can be used to verify the analog channel for radar signal processing at intermediate frequencies and the DSP algorithms used to estimate coordinates of the imitated target.

scholarly journals Improving passenger safety in cars using novel radar signal processing

2021 ◽  
Author(s):  
Hajar Abedi ◽  
Clara Magnier ◽  
Vishvam Mazumdar ◽  
George Shaker
Keyword(s):  
Signal Processing ◽  
Radar Signal ◽  
Radar Signal Processing ◽  
Passenger Safety
Sign in / Sign up

Export Citation Format

Share Document