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3DCFAR-Net: A Coherent Accumulation Network for Target Information Mining on Millimeter-Wave Radar
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DOI：10.23919/cje.2024.00.026.png)
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Abstract
En 中文
Millimeter-wave radar is a vital component of advanced driver assistance systems and intelligent transportation systems, thanks to its affordability, continuous operational capability, and resilience in adverse weather conditions. Point cloud imaging is one of the most crucial techniques that enables a thorough perception of the surroundings and enhances target detection and recognition in practical scenarios. However, conventional radar point cloud imaging methods encounter performance limitations due to inadequate target reflectivity under complex electromagnetic environments. This study investigates coherent and non-coherent accumulation methods for point cloud imaging and introduces a novel neural network method, namely, 3DCFAR-Net, to overcome these limitations. The robustness and effectiveness of 3DCFAR-Net are rigorously evaluated through both numerical simulations and real-world experiments under complex electromagnetic scenarios.
Keywords:
Aerospace and electronic systems
Antennas
Receiving antennas
Apertures
Antenna arrays
Transmitting antennas
Antennas and propagation
Phased arrays
Microwave antenna arrays
Microwave antennas
Three-dimensional constant false alarm rate (3D CFAR)
Coherent accumulation
Deep learning
Advanced driver assistance system
Millimeter-wave radar
Point cloud
Journal
C
IF:
3
Papers:
62
Citations:
1.7K
Organization
Cited Papers