A Compressed Sensing Recovery Algorithm Based on Support Set Selection

The theory of compressed sensing (CS) has shown tremendous potential in many fields, especially the signal processing area, due to its utility recovering unknown signals with far lower sampling rates than Nyquist frequency. In this paper, we present a novel, optimized recovery algorithm named supp-BPDN. proposed executes step selecting and recording support set original before using traditional mostly used called basis pursuit denoising (BPDN). We proved mathematically that even noise-affected CS system, probability still approaches 1, which means supp-BPDN can maintain good performance systems noise exists. Recovery results are demonstrated verify effectiveness superiority Besides, up photonic-enabled system realizing reconstruction two-tone peak frequency 350 MHz through 200 analog-to-digital converter (ADC) 1 GHz by 500 ADC. Similarly, showed better BPDN.