Global and Fast Refinement of Greedy Sensor Selection Algorithms for Linear Models

This letter focuses on greedy approaches to select the most informative <inline-formula><tex-math notation="LaTeX">k</tex-math></inline-formula> sensors from <inline-formula><tex-math notation="LaTeX">N</tex-math></inline-formula> c...

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Bibliographic Details
Published in:IEEE signal processing letters Vol. 32; pp. 2574 - 2578
Main Authors: Liu, Lingya, Wang, Yiyin, Hua, Cunqing
Format: Journal Article
Language:English
Published: IEEE 2025
Subjects:
Atmospheric measurements
Complexity theory
Covariance matrices
dominant submatrix
Eigenvalues and eigenfunctions
global search
Greedy algorithms
linear model
maximum-volume submatrix
Pollution measurement
Sensor selection
Signal processing algorithms
Temperature measurement
Training
Volume measurement
ISSN:1070-9908, 1558-2361
Online Access:Get full text
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Summary:This letter focuses on greedy approaches to select the most informative <inline-formula><tex-math notation="LaTeX">k</tex-math></inline-formula> sensors from <inline-formula><tex-math notation="LaTeX">N</tex-math></inline-formula> candidates to form a measurement submatrix that minimizes the estimation error. It is a submatrix selection problem. We refine conventional greedy sensor selection algorithms based on the square maximum-volume (SMV) submatrices finding method, particularly at their <inline-formula><tex-math notation="LaTeX">n</tex-math></inline-formula>th step, with <inline-formula><tex-math notation="LaTeX">n</tex-math></inline-formula> being the problem dimension. Our main idea is to increase the volume of the square measurement submatrix associated with the <inline-formula><tex-math notation="LaTeX">n</tex-math></inline-formula> sensors by iteratively swapping the selected and unselected sensors based on the dominant property of the maximum-volume submatrix. This simple refinement method ensures a square measurement matrix with increased volume, facilitating the subsequent greedy steps. It can be easily applied to existing greedy algorithms for performance improvement without increasing their complexity order. Numerical results demonstrate the effectiveness of the proposed refinement method in improving several popular greedy algorithms.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2025.3581492