OPTIMAL LINEAR COOPERATION FOR SIGNAL CLASSIFICATION

Quan, Zhi1; Ye, Muyang1,2; Ding, Zhi2; Cui, Shuguang2,3

10.1109/ICASSP.2016.7472354

2016

1520-6149

978-1-4799-9987-3

2016 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING PROCEEDINGS

2016-May

3631-3635

20-25 March 2016

Shanghai, China

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

In distributed inference, cooperation among networked agents can be exploited to enhance the performance of each individual agent. In this paper, we consider signal classification over a network of agents, where each agent observes a certain signal under a particular signal-to-noise ratio (SNR). Each agent produces a statistic that summarizes its observations over a time period and then forwards it to a fusion center for identifying the type of signal in a global manner. A linear cooperation strategy for signal classification is formulated as maximizing the classification probability subject to constrained misclassification probabilities. We show that this problem can be transformed into a convex problem under some conditions and linear cooperation is a simple but effective strategy that can greatly enhance the performance of signal classification over networked agents.

M-ary hypothesis testing signal classification data fusion distributed inference convex optimization

第一 ; 通讯

英语

CPCI-S ; EI

National Natural Science Foundation of China[61540044]

Acoustics ; Engineering

Acoustics ; Engineering, Electrical & Electronic

WOS:000388373403154

20162402489212

Web of Science

1.Southern Univ Sci & Technol, Dept Elect & Elect Engn, Shenzhen, Peoples R China
2.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai, Peoples R China
3.Texas A&M Univ, Dept Elect & Comp Engn, College Stn, TX 77843 USA

Quan, Zhi,Ye, Muyang,Ding, Zhi,et al. OPTIMAL LINEAR COOPERATION FOR SIGNAL CLASSIFICATION[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2016:3631-3635.