Algorithm 1 Location Vector Estimation

1: Initialization:

2: ${\gamma }_{\mathrm{th}}={10}^{-3}$, ${\tau }_{max}={10}^3$, ${\eta }_{\mathrm{th}}=-10\mathrm{dB}$, $\gamma =\tau =0$.

3: while ($\gamma ⩾{\gamma }_{\mathrm{th}}$ or $\tau ⩽{\tau }_{max}$) do

4:  Calculate ${\mathit{\mu }}^f$ and ${\mathsf{\Sigma }}^f$ by using Equation (16)–(19).

5:  Update $\mathit{\alpha }$ and $\beta$ by using Equation (21)–(22).

6:  $\gamma \leftarrow {\sum }_{f=1}^F{\parallel {\mathbf{y}}^f-{\mathsf{\Phi }}^f{\mathit{\mu }}^f\parallel }_2,\tau \leftarrow \tau +1$.

7: end while

8: Choose $\widehat{f}$ that minimizes $\parallel {\mathbf{y}}^f-{\mathsf{\Phi }}^f{\mathit{\mu }}^f{\parallel }_2$.

9: If $20lg\left({\mu }_n^{\widehat{f}}/\phantom{{\mu }_n^{\widehat{f}}\underset{i}{max}\left|{\mu }_i^{\widehat{f}}\right|}\underset{i}{max}\left|{\mu }_i^{\widehat{f}}\right|\right)<{\eta }_{\mathrm{th}}$, set ${\mu }_n^{\widehat{f}}=0$; otherwise, ${\mu }_n^{\widehat{f}}=1$.

10: Let the estimated location vector $\widehat{\mathit{s}}={\mathit{\mu }}^{\widehat{f}}$.