Compressive Sensing-Based SAR Image Reconstruction from Sparse Radar Sensor Data Acquisition in Automotive FMCW Radar...

Area of Science:

• Radar Systems Engineering
• Signal Processing
• Computational Imaging

Background:

• Traditional synthetic aperture radar (SAR) image reconstruction requires data acquired at regular spatial intervals.
• Inconsistent platform speeds, common in applications like automotive radar, disrupt regular data acquisition, complicating SAR imaging.
• Existing methods struggle to produce accurate SAR images when data sampling is non-uniform.

Purpose of the Study:

• To develop a novel method for reconstructing SAR images from sparsely and non-uniformly acquired radar sensor data.
• To address the limitations of conventional SAR image reconstruction algorithms in scenarios with variable platform speeds.
• To improve the efficiency and robustness of SAR image formation using compressive sensing.

Main Methods:

• Applied compressive sensing (CS) techniques, specifically l1-norm minimization, to reconstruct SAR images from non-uniform data.
• Replaced traditional Fourier and inverse Fourier transforms with CS-based signal recovery.
• Processed in-phase and quadrature components separately to mitigate phase distortion in the reconstructed signal.

Main Results:

• Successfully reconstructed SAR images from non-uniformly sampled data using the proposed CS method.
• Achieved a high correlation (0.83) for a SAR image reconstructed using only 70% of regularly acquired data.
• Demonstrated the effectiveness of the l1-norm minimization approach in handling non-uniform sampling.

Conclusions:

• The proposed CS-based method enables robust SAR image reconstruction even with irregular data acquisition.
• This technique overcomes the critical limitation of constant platform speed for SAR data collection.
• The findings suggest a significant advancement in SAR imaging flexibility and data acquisition strategies.