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.

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

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.)

DistMaP (2019)

Distributed and Massively Parallel Graph Algorithms

Read More  

HSS (2020)

Homomorphic Secret Sharing: Secure Computation and Beyond

Read More  

SUBMODULAR (2019)

The Power of Randomness and Continuity in Submodular Optimization

Read More