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.

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

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.)

CHIPTRANSFORM (2018)

On-chip optical communication with transformation optics

Read More  

QUAMAP (2019)

Quasiconformal Methods in Analysis and Applications

Read More  

CoolNanoDrop (2019)

Self-Emulsification Route to NanoEmulsions by Cooling of Industrially Relevant Compounds

Read More