Compressive Sampling with Known Spectral Energy Density

TL;DR

This paper proposes a spectral energy density-based sampling method to enhance compressive sensing performance in GPR signals, outperforming traditional uniform and random sampling techniques.

Contribution

It introduces an energy-aware sampling strategy that improves reconstruction quality in compressive sensing for GPR signals with known spectral energy distribution.

Findings

Energy equipartition sampling outperforms uniform and random sampling in PSNR.

Empirical results show improved reconstruction quality with spectral energy-based sampling.

Method enables higher acquisition speeds in SFCW radar applications.

Abstract

A method to improve l1 performance of the CS (Compressive Sampling) for A-scan SFCW-GPR (Stepped Frequency Continuous Wave-Ground Penetrating Radar) signals with known spectral energy density is proposed. Instead of random sampling, the proposed method selects the location of samples to follow the distribution of the spectral energy. Samples collected from three different measurement methods; the uniform sampling, random sampling, and energy equipartition sampling, are used to reconstruct a given monocycle signal whose spectral energy density is known. Objective performance evaluation in term of PSNR (Peak Signal to Noise Ratio) indicates empirically that the CS reconstruction of random sampling outperform the uniform sampling, while the energy equipartition sampling outperforms both of them. These results suggest that similar performance improvement can be achieved for the compressive SFCW (Stepped Frequency Continuous Wave) radar, allowing even higher acquisition speed.