Opendata, web and dolomites

DEEP-RADAR SIGNED

Learning efficient millimeter wave radar imaging for autonomous vehicles

Total Cost €

0

EC-Contrib. €

0

Partnership

0

Views

0

 DEEP-RADAR project word cloud

Explore the words cloud of the DEEP-RADAR project. It provides you a very rough idea of what is the project "DEEP-RADAR" about.

frame    phased    technologies    ratio    receiver    spatial    sufficient    pipeline    alternative    automotive    entire    protocols    medical    antennas    signal    transmits    transmitted    mimo    insufficient    decade    transmitting    optical    images    industry    millimeter    ultrasonography    demonstrated    moving    wave    patterns    sensing    channels    learning    imaging    description    autonomous    demonstrating    40    configuration    billion    methodology    radars    relying    weather    2050    noise    pulses    trillion    driving    physics    modalities    despite    array    grow    transmit    costly    485    output    self    shape    containing    requirement    commercial    shorter    prohibitively    hitting    significantly    halve    digital    input    share    penetrate    2026    learned    velocity    restricted    receivers    cagr    multiple    overcome    conceptual    vehicle    temporal    reading    almost    reducing    ecosystem    attractive    consensus    underlying    viability    similarities    cars    receive    mathematical    resolution    adverse    quality    intend    ultrasound    reconstruction    reduce    exceeding    expensive    imperative    accurate    weakness    rate    parts    compromising    radar    maintaining    proof    image    smaller   

Project "DEEP-RADAR" data sheet

The following table provides information about the project.

Coordinator
TECHNION - ISRAEL INSTITUTE OF TECHNOLOGY 

Organization address
address: SENATE BUILDING TECHNION CITY
city: HAIFA
postcode: 32000
website: www.technion.ac.il

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Israel [IL]
 Total cost 150˙000 €
 EC max contribution 150˙000 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2019-PoC
 Funding Scheme ERC-POC-LS
 Starting year 2019
 Duration (year-month-day) from 2019-10-01   to  2021-03-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    TECHNION - ISRAEL INSTITUTE OF TECHNOLOGY IL (HAIFA) coordinator 150˙000.00

Map

 Project objective

The emerging autonomous vehicle ecosystem is expected to grow with an almost 40% CAGR in the next decade hitting €485 billion by 2026 and exceeding €6 trillion in 2050. There is wide industry consensus that improved long-range depth sensing modalities are imperative for the viability of self-driving cars. State-of-the-art optical technologies are still prohibitively expensive, have insufficient temporal and spatial resolution, do not provide an accurate velocity reading, and are restricted to a shorter range in adverse weather conditions. Millimeter wave multiple-input multiple-output (MIMO) radars are an attractive alternative relying on a phased array of transmitting antennas and digital receivers, containing no moving parts, and able to penetrate adverse weather conditions. The weakness of this technology is the costly requirement for a large number of receiver channels to achieve sufficient spatial resolution. We will apply our novel methodology recently developed for medical imaging to overcome this challenge.

We have demonstrated that learning the entire imaging pipeline in medical ultrasonography, including the shape of the transmitted pulses and the configuration of the receivers allows reducing the number of transmits by a factor of 3, while maintaining image quality comparable to traditional high-frame rate imaging protocols. Despite the different underlying physics, ultrasound and radar imaging share many conceptual similarities and have a similar mathematical description. Here, we intend to develop a proof-of-concept MIMO radar system demonstrating that by using the learned transmit patterns and image reconstruction pipeline, it is possible to halve the number of receive channels without compromising the image resolution and signal-to-noise ratio. Maintaining high resolution images using a smaller number of receiver channels will significantly reduce the cost of this technology and increase the commercial viability of automotive MIMO radars.

Are you the coordinator (or a participant) of this project? Plaese send me more information about the "DEEP-RADAR" project.

For instance: the website url (it has not provided by EU-opendata yet), the logo, a more detailed description of the project (in plain text as a rtf file or a word file), some pictures (as picture files, not embedded into any word file), twitter account, linkedin page, etc.

Send me an  email (fabio@fabiodisconzi.com) and I put them in your project's page as son as possible.

Thanks. And then put a link of this page into your project's website.

The information about "DEEP-RADAR" are provided by the European Opendata Portal: CORDIS opendata.

More projects from the same programme (H2020-EU.1.1.)

ERC VP CSA (2018)

Support to the Vice-Presidents of the ERC Scientific Council 2018

Read More  

AST (2019)

Automatic System Testing

Read More  

RTMFRM (2019)

Room Temperature Magnetic Resonance Force Microscopy

Read More