Overview of Vehicle-mounted 4D Millimetre Wave Radar Technology
DOI:
https://doi.org/10.54097/va83k336Keywords:
4D millimeter-wave radar, super-resolution ranging, constant false alarm rate (CFAR), DBSCAN clustering algorithm, adaptive parameter optimizationAbstract
In response to the urgent need for ranging resolution, real-time target detection, and point cloud clustering in high-precision 4D millimeter-wave radar imaging, this paper systematically reviews the research progress of key signal processing algorithms. Regarding ranging accuracy, while spectrum refinement algorithms (such as ZFFT and CZT) and super-resolution algorithms (such as MUSIC and compressed sensing) improve resolution, they generally suffer from high computational complexity and insufficient utilization of phase information. In the field of constant false alarm rate (CFAR), mean-value algorithms (CA-CFAR) offer excellent real-time performance but weak multi-target detection capabilities, while ordered statistics algorithms (OS-CFAR) offer strong interference tolerance but require optimization of the adaptive range threshold. Among clustering algorithms, the improved 3D PG-DBSCAN overcomes the global density limitations of traditional DBSCAN, but its static grid parameter setting still restricts its adaptability to dynamic scenes. Based on this, this paper proposes a coherent information-fused CZT-Rife joint ranging algorithm, a range-adaptive ED-CFAR detection strategy, and a 3D PG-DBSCAN optimization scheme with dynamic grid parameters, providing theoretical support for high-precision real-time processing in automotive millimeter-wave radars.
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