Partially adaptive filtering using randomized projections

Olivier Besson

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arXiv · 2203.10873

Partially adaptive filtering using randomized projections

Olivier Besson

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Abstract

This short note addresses the design of a partially adaptive filter to retrieve a signal of interest in the presence of strong low-rank interference and thermal noise. We consider a generalized sidelobe canceler implementation where the dimension-reducing transformation is build resorting to ideas borrowed from randomized matrix approximations. More precisely, the main subspace of the auxiliary data $Z$ is approximated by $Z\Omega$ where $\Omega$ is a random matrix or a matrix that picks at random columns of $Z$. These transformations do not require eigenvalue decomposition, yet they provide performance similar to those of a principal component filter.

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BibTeX RIS

Olivier Besson. 2022-03-21. Partially adaptive filtering using randomized projections. https://arxiv.org/abs/2203.10873

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