Sparse subspace averaging for order estimation
| Title | Sparse subspace averaging for order estimation |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Garg, V., D. Ramírez, and I. Santamaría |
| Conference Name | IEEE Statistical Signal Processing Workshop |
| Month Published | July |
| Abstract | This paper addresses the problem of source enumeration for arbitrary geometry arrays in the presence of spatially correlated noise. The method combines a sparse reconstruction (SR) step with a subspace averaging (SA) approach, and hence it is named sparse subspace averaging (SSA). In the first step, each received snapshot is approximated by a sparse linear combination of the rest of snapshots. The SR problem is regularized by the logarithm-based surrogate of the l0-norm and solved using a majorization-minimization approach. Based on the SR solution, a sampling mechanism is proposed in the second step to generate a collection of subspaces, all of which approximately span the same signal subspace. Finally, the dimension of the average of this collection of subspaces provides a robust estimate for the number of sources. Our simulation results show that SSA provides robust order estimates under a variety of noise models. |
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