Signal Number Estimation based on Support Vector Machine

doi:10.23940/ijpe.20.10.p17.16551664

Int J Performability Eng ›› 2020, Vol. 16 ›› Issue (10): 1655-1664.doi: 10.23940/ijpe.20.10.p17.16551664

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Signal Number Estimation based on Support Vector Machine

Jiaqi Zhen^*, and Xiaoli Zhang

College of Electronic Engineering, Heilongjiang University, Harbin, 150080, China

Submitted on ; Revised on ; Accepted on
Contact: * E-mail address: zhenjiaqi@hlju.edu.cn
About author:Jiaqi Zhen is an Associate Professor in the major of the Internet of Things engineering with Heilongjiang University, and the Director of the Institute of Instrumentation. His research interests include spatial spectrum estimation, indoor positioning, array signal parameter estimation, and embedded system design.
Xiaoli Zhang is currently pursuing the master's degree with Heilongjiang University. Her research interest include super-resolution direction finding technology and spatial spectrum estimation.

Abstract

Abstract: In order to reduce the calculation burden of the signal number estimation and improve the accuracy against the background of small snapshots, an idea for counting signal numbers based on a support vector machine is provided. First, the features of the signal and noise are extracted by the orthogonality between the noise vector and the array manifold. Then, a classifier based on a support vector machine is designed. Finally, the optimal structure of the classifier and the corresponding coefficients are trained by theoretical analysis and training data. The proposed algorithm performs well in both Gaussian white noise and colored noise. The validity and feasibility of the proposed theory are verified by the simulations.

Key words: array signal processing, signal number estimation, support vector machines, small snapshots

Jiaqi Zhen, and Xiaoli Zhang. Signal Number Estimation based on Support Vector Machine [J]. Int J Performability Eng, 2020, 16(10): 1655-1664.

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Signal Number Estimation based on Support Vector Machine

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