Algorithm 1 Orthogonal matching pursuit (OMP)

Input: matrix $\varphi$, measurements y, sparsity K

Output: sparse reconstruction $x^K$

1: $r^0=y$ and ${\Gamma }^0=\varnothing$

2: for$\mathrm{i}=1$…, K do

3: ${\lambda }^{\mathrm{i}}\Leftarrow argma{x}_j|\langle r^{i-1},{\varphi }_j\rangle |$ Find best fitting column

4: ${\Gamma }^0\Leftarrow {\Gamma }^{i-1}{{\displaystyle \cup }}^{\hspace{1em}} {\lambda }^{\mathrm{i}}$

5: $x^i\Leftarrow argmi{n}_x{\left|\right|r^{i-1}-{\varphi }_{{\Gamma }^i}x\left|\right|}_2^2$ LS optimization

6: $r^i\Leftarrow$ $r^{i-1}-{\varphi }_{{\Gamma }^i}{x}^i$ Residual update

7: end for