A Hybrid Quantization Method for FMCW Radar Data Compression

FMCW radar is widely used in the field of autonomous driving. However, the high-resolution signal processing in FMCW radar presents challenges for on-chip memory and transmission bandwidth. Thus, We propose a compression method that integrates a lattice vector quantization scheme with non-uniform amplitude quantization, while employing uniform quantization for phase. During amplitude quantization, the algorithm first performs grouped quantization on spectral data to mitigate the impact of high-amplitude values on quantization accuracy. It then applies vector normalization to reduce the hardware resources required for storing shared parameters, and selects the quantization approach based on the numerical distribution characteristics. All modules are implemented with hardware-friendly operations to strike a balance between efficiency and feasibility. The experimental results demonstrate that our algorithm achieves a 74.8% reduction in storage usage while maintaining a reconstructed signal PSNR of 40 dB, and requires only a minimal amount of additional computational resources to ensure its object detection performance.