scholarly journals Software Radar signal processing

2005 ◽  
Vol 23 (1) ◽  
pp. 109-121 ◽  
Author(s):  
T. Grydeland ◽  
F. D. Lind ◽  
P. J. Erickson ◽  
J. M. Holt
Keyword(s):  
Signal Processing ◽  
Cross Correlation ◽  
General Purpose ◽  
Radar Signal ◽  
Software Infrastructure ◽  
Computing Systems ◽  
Radio Science ◽  
Software Patterns ◽  
Incoherent Scatter ◽  
Radar Systems

Abstract. Software infrastructure is a growing part of modern radio science systems. As part of developing a generic infrastructure for implementing Software Radar systems, we have developed a set of reusable signal processing components. These components are generic software-based implementations for use on general purpose computing systems. The components allow for the implementation of signal processing chains for radio frequency signal reception, correlation-based data processing, and cross-correlation-based interferometry. The components have been used to implement the signal processing necessary for incoherent scatter radar signal reception and processing as part of the latest version of the Millstone Hill Data Acquisition System (MIDAS-W). Several hardware realizations with varying capabilities have been created, and these have been used successfully with different radars. We discuss the signal processing components in detail, describe the software patterns in which they are used, and show example data from the Millstone Hill, EISCAT Svalbard, and SOUSY Svalbard radars.

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.

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>

A Multi-Channel Radar Signal Process System

2010 ◽  
Vol 20-23 ◽  
pp. 884-888
Author(s):  
Cheng Fa Xu ◽  
Jun Ling Wang ◽  
Rong Gang Wu
Keyword(s):  
Signal Processing ◽  
High Performance ◽  
Processing System ◽  
General Purpose ◽  
Radar Signal ◽  
Radar Signal Processing ◽  
Signal Processing System ◽  
Signal Processing Algorithm ◽  
High Data ◽  
Advantages And Disadvantages

In order to meet multi-channel, high data rate, intensive computing capacity of modern radar signal processing, a standard, scalable, high-performance general-purpose radar signal processing system platform is proposed. The main processor of this system platform is the DSP and FPGA. In the analysis of different kinds of radar signal processing algorithm, and taking into account the respective advantages and disadvantages of DSP and FPGA, In this paper, a software architecture method for radar signal processing is given to decide how to distribute different algorithm into DSP and FPGA. At last, for a certain type of circular array radar, an implementation of radar signal processing by using the general-purpose radar signal processing system platform is proposed.

scholarly journals Broadband radar invisibility with time-dependent metasurfaces

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
V. Kozlov ◽  
D. Vovchuk ◽  
P. Ginzburg
Keyword(s):  
Signal Processing ◽  
Metal Plate ◽  
Primary Objective ◽  
Radar System ◽  
Radar Signal ◽  
Surveillance Systems ◽  
Radar Systems ◽  
Reliable Performance ◽  
Promising Solution ◽  
Radar Countermeasure

AbstractConcealing objects from interrogation has been a primary objective since the integration of radars into surveillance systems. Metamaterial-based invisibility cloaking, which was considered a promising solution, did not yet succeed in delivering reliable performance against real radar systems, mainly due to its narrow operational bandwidth. Here we propose an approach, which addresses the issue from a signal-processing standpoint and, as a result, is capable of coping with the vast majority of unclassified radar systems by exploiting vulnerabilities in their design. In particular, we demonstrate complete concealment of a 0.25 square meter moving metal plate from an investigating radar system, operating in a broad frequency range approaching 20% bandwidth around the carrier of 1.5 GHz. The key element of the radar countermeasure is a temporally modulated coating. This auxiliary structure is designed to dynamically and controllably adjust the reflected phase of the impinging radar signal, which acquires a user-defined Doppler shift. A special case of interest is imposing a frequency shift that compensates for the real Doppler signatures originating from the motion of the target. In this case the radar will consider the target static, even though it is moving. As a result, the reflected echo will be discarded by the clutter removal filter, which is an inherent part of any modern radar system that is designed to operate in real conditions. This signal-processing loophole allows rendering the target invisible to the radar even though it scatters electromagnetic radiation.

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