Compressive Detection of Random Signals from Sparsely Corrupted Measurements

被引：0

作者：

Tian, Yun ^{[1
]}

Xu, Wenbo ^{[2
]}

Qin, Jing ^{[1
]}

Zhao, Xiaofan ^{[1
]}

机构：

[1] Peoples Publ Secur Univ China, Coll Informat Technol & Cyber Secur, Beijing 100038, Peoples R China

[2] Beijing Univ Posts & Telecommun, Minist Educ, Key Lab Universal Wireless Commn, Beijing 100876, Peoples R China

来源：

PROCEEDINGS OF 2018 INTERNATIONAL CONFERENCE ON NETWORK INFRASTRUCTURE AND DIGITAL CONTENT (IEEE IC-NIDC) | 2018年

基金：

中国国家自然科学基金;

关键词：

Compressed sensing; Detection; Sparse error; Compressive detection;

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：

Compressed sensing (CS) integrates sampling and compression into a single step to reduce the processed data amount. However, the CS reconstruction generally suffers from high complexity. To solve this problem, compressive signal processing (CSP) is recently proposed to implement some signal processing tasks directly in the compressive domain without reconstruction. Among various CSP techniques, compressive detection achieves the signal detection based on the CS measurements. This paper investigates the compressive detection problem of random signals when the measurements are corrupted. Different from the current studies that only consider the dense noise, our study considers both the dense noise and sparse error. The theoretical performance is derived, and simulations are provided to verify the derived theoretical results.

引用

页码：389 / 393

页数：5

共 14 条

[1] A Simple Proof of the Restricted Isometry Property for Random Matrices [J].