Matched Detection in Union of Low-rank Subspaces

被引：0

作者：

Joneidi, M. ^{[1
]}

Sadeghi, M. ^{[1
]}

Ahmadi, P. ^{[1
]}

Golestani, H. B. ^{[1
]}

Ghanbari, M. ^{[1
]}

机构：

[1] Inst Res Fundamental Sci IPM, Tehran, Iran

来源：

2014 7TH INTERNATIONAL SYMPOSIUM ON TELECOMMUNICATIONS (IST) | 2014年

关键词：

Union of subspaces model; sparse representation; signal detection; dictionary learning;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, a new detection approach based on sparse decomposition in terms of a union of learned subspaces is presented. It uses a dictionary that can be interpreted as a bank of matched subspaces. This improves the performance of signal detection, as it is a generalization for detectors and also exploits sparsity in its decision rule. The proposed detector shows a new trade-off for designing a suitable detector. We demonstrate he high efficiency of our method in the case of voice activity detection in speech processing.

引用

页码：371 / 374

页数：4

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