ICASSP 2022accepted0 citations

Coarray Manifold Separation In The Spherical Harmonics Domain For Enhanced Source Localization

Shekhar Kumar Yadav, Nithin V. George

Abstract

The order of a three-dimensional wavefield captured by a spherical array is limited by the number of sampling points i.e. the number of sensors in the array. This restricts the source localization performance of existing techniques for a spherical array. In this paper, we introduce the concept of difference coarray to spherical arrays and propose an algorithm which utilises the increased degrees-of-freedom (DOF) provided by the virtual coarray sensors to perform enhanced source localization. We make use of coarray manifold separation in the spherical harmonics domain to generate a Vandermonde structured coarray manifold matrix which allows us to propose a novel subspace-based algorithm, which we call the coarraySH-MUSIC. We also introduce a polynomial rooting version of our algorithm which does not rely on extensive grid searches. The proposed algorithms are evaluated using various simulated experiments on source localization.

BibTeX
@inproceedings{icassp2022_coarraymanifolds,
  title = {Coarray Manifold Separation In The Spherical Harmonics Domain For Enhanced Source Localization},
  author = {Shekhar Kumar Yadav and Nithin V. George},
  booktitle = {ICASSP 2022},
  year = {2022}
}
Coarray Manifold Separation In The Spherical Harmonics Domain For Enhanced Source Localization · ICASSP 2022