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High-Resolution and Large-Detection-Range Virtual Antenna Array for Automotive Radar Applications.

Haythem Abdullah1, Mohamed Mabrouk2, Ahmed Abd-Elnaby Kabeel3

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This study presents a new virtual antenna array (VAA) for train collision-avoidance systems (TCAS). The VAA enhances object detection range and resolution, improving safety for autonomous vehicles.

Keywords:
automotive radarlinear antenna arraylong-range radarmedium-range radarvirtual antenna array

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Area of Science:

  • Electrical Engineering
  • Radar Systems
  • Antenna Theory

Background:

  • Autonomous vehicles require advanced collision avoidance systems for safety.
  • Virtual antenna arrays (VAA) offer potential for enhanced radar performance.
  • Existing VAA systems face challenges with power loss and reduced detection range.

Purpose of the Study:

  • To introduce a novel generic scheme for a virtual antenna array (VAA).
  • To apply the VAA in a train collision-avoidance system (TCAS) for object detection.
  • To address limitations of current VAA systems, focusing on range and resolution.

Main Methods:

  • Developed a new virtual array distribution for transmitting and receiving antennas.
  • Integrated four AWR1243 RF chips operating at 76-81 GHz using 45 nm RFCMOS technology.
  • Designed a high-gain, low side lobe level VAA system for automotive radar applications.

Main Results:

  • Achieved a measured VAA gain of 47.2 dBi, significantly higher than traditional radar systems.
  • Demonstrated a 45% reduction in implementation area compared to existing LFMCW antenna arrays.
  • Validated simulated and measured VAA patterns, showing high matching in beamwidth and side lobe levels.

Conclusions:

  • The proposed VAA scheme effectively enhances object detection range and resolution for TCAS.
  • The practical implementation demonstrates a high-gain, low side lobe VAA system with improved efficiency.
  • This VAA technology contributes to safer and more reliable autonomous vehicle operation.