The recovery algorithm and sparse bases are BSBL and DCT, respectively: N = 512, M = 256, and K = 4; Pan-Tompkins method  was used for R-peak detection in the recovery signals.
PRD (%) Experimental conditions Lay Sit Stand Walk Run BSBL + WT  6.35 6.20 9.58 5.66 7.23 BSBL + DCT  3.54 2.28 2.91 1.39 2.52 OMP + WT  7.69 10.35 21.34 12.01 11.85 L1 + WT  10.68 11.67 20.05 16.30 11.80 Table 2: Comparison of the proposed node with commercial ones.
Then x is block sparse and can be solved in BSBL* framework Both BSBL and OG-VSBL formulate the off-grid RCI using Bayesian hierarchical prior modeling, but their implementation processes are different.
These algorithms for comparison include BLOOMP, JCP, S-TLS, and BSBL algorithms.
Comparably, the image reconstructed by BSBL is much sparser as presented in Figure 6(e), but a few spurious scatterers still exist.
As presented in Table 1, BSBL and OG-VSBL can achieve much sparser image due to the utilization of the hierarchical Bayesian modeling; thus the image quality is improved significantly.
Caption: Figure 6: Off-grid RCI results: (a) original image; (b) BLOOMP; (c) JCP; (d) S-TLS; (e) BSBL; (f) OG-VSBL.