OPTIMAL EIGENVALUE DECOMPOSITION BASED FREQUENCY ESTIMATION ALGORITHM FOR COMPLEX SINUSOIDAL SIGNALS

被引：0

作者：

Zubair, Muhammad ^{[1
]}

Ahmed, Sajid ^{[1
]}

Jardak, Seifallah ^{[2
]}

Alouini, Mohamed-Slim ^{[2
]}

机构：

[1] ITU, Elect Engn Dept, Lahore, Pakistan

[2] KAUST, Comp Elect Math Sci & Engn, Thuwal, Saudi Arabia

来源：

2018 IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (GLOBALSIP 2018) | 2018年

关键词：

Frequency Estimation; Denoising; MUSIC; KLT; SINGLE; PARAMETERS; PHASE;

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：

The estimation performance of subspace based algorithms depends on the selection of dominant eigenvalues, which is challenging. In this paper, by exploiting the circular transformation, an optimal dominant eigenvalue selection subspace based frequency estimation algorithm is proposed. The proposed algorithm restricts the contribution of signal into fixed number of dominant eigenvalues. The performance of the proposed algorithm is compared with Multiple Signal Classification (MUSIC) and Karhunen-Loeve-transform (KLT) algorithms. The analytical and simulation results show that proposed algorithm outperforms the MUSIC and KLT algorithms.

引用

页码：1119 / 1123

页数：5

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