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DEEP-RADAR SIGNED

Learning efficient millimeter wave radar imaging for autonomous vehicles

Total Cost €

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EC-Contrib. €

0

Partnership

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 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.

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

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.

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

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