Signal Processing by Digital Generalized Detector in Complex Radar Systems

2017 ◽  
pp. 25-56
Author(s):  
Vyacheslav Tuzlukov
Keyword(s):  
Signal Processing ◽  
Radar Systems

Automotive Radars

2017 ◽  
Author(s):  
Sujeet Patole ◽  
Murat Torlak ◽  
Dan Wang ◽  
Murtaza Ali
Keyword(s):  
Signal Processing ◽  
Pedestrian Detection ◽  
Radar Signal ◽  
Automotive Radar ◽  
Estimation Algorithms ◽  
Radar Systems ◽  
Starting Point ◽  
Processing Techniques ◽  
Automotive Radars ◽  
Signal Processing Techniques

Automotive radars, along with other sensors such as lidar, (which stands for “light detection and ranging”), ultrasound, and cameras, form the backbone of self-driving cars and advanced driver assistant systems (ADASs). These technological advancements are enabled by extremely complex systems with a long signal processing path from radars/sensors to the controller. Automotive radar systems are responsible for the detection of objects and obstacles, their position, and speed relative to the vehicle. The development of signal processing techniques along with progress in the millimeter- wave (mm-wave) semiconductor technology plays a key role in automotive radar systems. Various signal processing techniques have been developed to provide better resolution and estimation performance in all measurement dimensions: range, azimuth-elevation angles, and velocity of the targets surrounding the vehicles. This article summarizes various aspects of automotive radar signal processing techniques, including waveform design, possible radar architectures, estimation algorithms, implementation complexity-resolution trade-off, and adaptive processing for complex environments, as well as unique problems associated with automotive radars such as pedestrian detection. We believe that this review article will combine the several contributions scattered in the literature to serve as a primary starting point to new researchers and to give a bird’s-eye view to the existing research community.

A New Radar Signal Processing Architecture Based on Multi-Core Processor

2014 ◽  
Vol 556-562 ◽  
pp. 1618-1621
Author(s):  
Jia Liang Fan ◽  
Qiang Yang
Keyword(s):  
Signal Processing ◽  
Pulse Compression ◽  
Adaptive Beamforming ◽  
System Structure ◽  
Radar Signal ◽  
Radar Signal Processing ◽  
Test Results ◽  
Radar Systems ◽  
Multi Core Processor ◽  
Processing Architecture

Most radar systems based on the structure that contains many DSP chips. The system structure is always complex, and it is difficult to update. Nowadays, multi-core processor develops very fast. Compared with DSP chips, multi-core processor has better performance in signal processing field. In this paper, we present a signal processing architecture which based on multi-core processor. Pulse compression algorithms and PCI-E bus are discussed as two important technologies. Adaptive beamforming test results show that multi-core processor is able to achieve radar signal processing.

Hacotization of discretization and quantization conditions in radar systems with digital phased antenna grates

2021 ◽  
Author(s):  
Yu.N. Gorbunov ◽  
G.L. Akopyan ◽  
R.K. Burnosov
Keyword(s):  
Signal Processing ◽  
Phased Array ◽  
Frequency Selectivity ◽  
Time Signal ◽  
Stochastic Quantization ◽  
Phased Array Antennas ◽  
Radar Systems ◽  
Array Antennas ◽  
Time Space ◽  
Spatial Frequencies

The article establishes a connection between the directional properties of digital phased array antennas (FAR) in radar systems by means of space-time signal processing, based on the use of stochastic quantization scales in time, space and level, leading to a decrease in the influence of sampling noise and quantization in the process of diagram formation and the formation of frequency selectivity "narrowed" (in the usual and spatial frequencies - angular directions) frequency channels, formed by increasing the size of spatial and temporal samples.

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>

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