Redundancy Reduction for Compressed Sensing based Random Equivalent Sampling Signal Reconstruction

Random equivalent sampling (RES) can composite a waveform with high equivalent sampling rate from multiple low speed sampling sequences. In practical application, the performance of RES signal reconstruction would be degraded by the non-uniform distribution of sampling time. Compressed sensing (CS) theory is adopted to reconstruct RES samples, which could mitigate the inherent coherence of sampling time. However, the CS reconstruction algorithm is sensitive to the signal sparsity level that is unknown in the reconstruction stage. In this paper, we propose a redundancy reduction algorithm for CS base RES signal reconstruction that can ensure reconstruction accuracy while reducing the number of random samples. The experimental results are reported to evaluate the performance of the proposed algorithm.