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DEEP-RADAR SIGNED

Learning efficient millimeter wave radar imaging for autonomous vehicles

Total Cost €

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EC-Contrib. €

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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.

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

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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