Work

Compressed sensing matrices from orthogonal spaces over finite fields of odd characteristic

Year: 2026

Type: article

Abstract: In this paper, we construct deterministic matrices from subspaces of orthogonal spaces over finite fields of odd characteristic and investigate their applicability to compressed sensing. The construction is based on incidence relations among three types of subspaces, yielding families of matrices with explicitly computable dimensions and coherence. Using coherence-based estimates, we establish sufficient conditions under which these matrices satisfy the Restricted Isometry Property for prescribed sparsity levels. We also provide numerical comparisons with DeVore’s deterministic construction to illustrate the trade-off between the number of measurements, coherence, and sparse recovery guarantees.

Source: Journal of Algebra and Its Applications

DOI: 10.1142/s0219498828500405

Authors: Kanittakorn Moonchaisook, Poom Kumam, Songpon Sriwongsa

Institution: King Mongkut's University of Technology Thonburi

Language: English

Topic: Sparse and Compressive Sensing Techniques

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: National Research Council of Thailand, King Mongkut's University of Technology Thonburi, Center of Excellence in Theoretical and Computational Science, King Mongkut's University of Technology Thonburi

Award: G6549519596