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.

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

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  

CHIPTRANSFORM (2018)

On-chip optical communication with transformation optics

Read More  

CohoSing (2019)

Cohomology and Singularities

Read More