Radar Signal Sorting and Recognition Based on Transferred Deep Learning

<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>

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

10.36227/techrxiv.13640861.v1 ◽

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>

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Deep Learning-Based Approach for Low Probability of Intercept Radar Signal Detection and Classification

Journal of Communications Technology and Electronics ◽

10.1134/s1064226920100034 ◽

2020 ◽

Vol 65 (10) ◽

pp. 1179-1191

Author(s):

G. Ghadimi ◽

Y. Norouzi ◽

R. Bayderkhani ◽

M. M. Nayebi ◽

S. M. Karbasi

Keyword(s):

Deep Learning ◽

Signal Detection ◽

Radar Signal ◽

Low Probability ◽

Low Probability Of Intercept

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Multi-Input Deep Learning Based FMCW Radar Signal Classification

Electronics ◽

10.3390/electronics10101144 ◽

2021 ◽

Vol 10 (10) ◽

pp. 1144

Author(s):

Daewoong Cha ◽

Sohee Jeong ◽

Minwoo Yoo ◽

Jiyong Oh ◽

Dongseog Han

Keyword(s):

Deep Learning ◽

Point Cloud ◽

Continuous Wave ◽

Autonomous Driving ◽

Radar Data ◽

Classification Performance ◽

Radar Signal ◽

Fmcw Radar ◽

Input Structure ◽

Single Input

In autonomous driving vehicles, the emergency braking system uses lidar or radar sensors to recognize the surrounding environment and prevent accidents. The conventional classifiers based on radar data using deep learning are single input structures using range–Doppler maps or micro-Doppler. Deep learning with a single input structure has limitations in improving classification performance. In this paper, we propose a multi-input classifier based on convolutional neural network (CNN) to reduce the amount of computation and improve the classification performance using the frequency modulated continuous wave (FMCW) radar. The proposed multi-input deep learning structure is a CNN-based structure using a distance Doppler map and a point cloud map as multiple inputs. The classification accuracy with the range–Doppler map or the point cloud map is 85% and 92%, respectively. It has been improved to 96% with both maps.

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Application of Deep Learning on Millimeter-Wave Radar Signals: A Review

Sensors ◽

10.3390/s21061951 ◽

2021 ◽

Vol 21 (6) ◽

pp. 1951

Author(s):

Fahad Jibrin Abdu ◽

Yixiong Zhang ◽

Maozhong Fu ◽

Yuhan Li ◽

Zhenmiao Deng

Keyword(s):

Deep Learning ◽

Radar Data ◽

Radar Signal ◽

Future Research ◽

Learning Technology ◽

Learning Approaches ◽

Learning Models ◽

Wave Radar ◽

Adverse Weather ◽

Radar Signals

The progress brought by the deep learning technology over the last decade has inspired many research domains, such as radar signal processing, speech and audio recognition, etc., to apply it to their respective problems. Most of the prominent deep learning models exploit data representations acquired with either Lidar or camera sensors, leaving automotive radars rarely used. This is despite the vital potential of radars in adverse weather conditions, as well as their ability to simultaneously measure an object’s range and radial velocity seamlessly. As radar signals have not been exploited very much so far, there is a lack of available benchmark data. However, recently, there has been a lot of interest in applying radar data as input to various deep learning algorithms, as more datasets are being provided. To this end, this paper presents a survey of various deep learning approaches processing radar signals to accomplish some significant tasks in an autonomous driving application, such as detection and classification. We have itemized the review based on different radar signal representations, as it is one of the critical aspects while using radar data with deep learning models. Furthermore, we give an extensive review of the recent deep learning-based multi-sensor fusion models exploiting radar signals and camera images for object detection tasks. We then provide a summary of the available datasets containing radar data. Finally, we discuss the gaps and important innovations in the reviewed papers and highlight some possible future research prospects.

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