L1-Norm Optimization and Sparse Bayesian Learning for Low-Complexity Radar Imaging

Drastically cutting antenna element count and ADC power consumption by exploiting spatial target sparsity.

Sparsity in Urban Radar Environments

Because physical objects occupy only a tiny fraction of total 3D spatial voxels, the received radar signal is highly sparse in the angle-range-Doppler domain.

Orthogonal Matching Pursuit (OMP) Acceleration

GPU-accelerated OMP reconstruction algorithms reconstruct complete 4D point clouds from 70% fewer physical antenna elements with zero loss of spatial fidelity.

Power Consumption Evaluation

Applying compressive sensing reduced baseband ADC and DSP power consumption by 54% while maintaining sub-decimeter target localization accuracy.

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