Open AccessUnion of low‐rank subspaces detector
Author(s) -
Joneidi Mohsen,
Ahmadi Parvin,
Sadeghi Mostafa,
Rahnavard Nazanin
Publication year - 2016
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
ISSN - 1751-9683
DOI - 10.1049/iet-spr.2015.0009
Subject(s) - linear subspace , detector , rank (graph theory) , computer science , signal (programming language) , detection theory , signal processing , sparse approximation , flexibility (engineering) , pattern recognition (psychology) , representation (politics) , artificial intelligence , algorithm , speech recognition , mathematics , digital signal processing , statistics , telecommunications , politics , political science , geometry , combinatorics , computer hardware , law , programming language
The problem of signal detection using a flexible and general model is considered. Owing to applicability and flexibility of sparse signal representation and approximation, it has attracted a lot of attention in many signal processing areas. In this study, the authors propose a new detection method based on sparse decomposition in a union of subspaces model. Their proposed detector uses a dictionary that can be interpreted as a bank of matched subspaces. This improves the performance of signal detection, as it is a generalisation for detectors. Low‐rank assumption for the desired signals implies that the representations of these signals in terms of some proper bases would be sparse. Their proposed detector exploits sparsity in its decision rule. They demonstrate the high efficiency of their method in the cases of voice activity detection in speech processing.