PHD Filtering for Multi-Source DOA Tracking With Extended Co-Prime Array: An Improved MUSIC Pseudo-Likelihood

被引：13

作者：

Zhao, Jun ^{[1
]}

Gui, Renzhou ^{[2
]}

Dong, Xudong ^{[3
]}

机构：

[1] Tongji Univ, Coll Elect & Informat Engn, Shanghai 201804, Peoples R China

[2] Tongji Univ, Dept Elect & Informat, Shanghai 201804, Peoples R China

[3] Nanjing Univ Aeronaut & Astronaut, Coll Elect & Informat Engn, Nanjing 211100, Peoples R China

来源：

IEEE COMMUNICATIONS LETTERS | 2021年 / 25卷 / 10期

基金：

中国国家自然科学基金;

关键词：

Direction-of-arrival estimation; Radar tracking; Estimation; Covariance matrices; Radio frequency; Eigenvalues and eigenfunctions; Multiple signal classification; Direction-of-arrival (DOA) tracking; probability hypothesis density (PHD); likelihood function; co-prime array; ALGORITHM;

D O I：

10.1109/LCOMM.2021.3099569

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

To solve the problem of multi-source direction of arrival (DOA) tracking in co-prime array, a multi-source DOA tracking algorithm based on probability hypothesis density (PHD) filtering is proposed, which can adapt to the scenario where DOA and number of sources change with time. In this letter, we use the minimum description length (MDL) method to estimate the number of sources and construct a new noise subspace by performing eigenvalue decomposition (EVD) on the reconstructed signal subspace. An improved multiple signal classification (MUSIC) pseudo-spectrum is utilized to calculate the likelihood function of the proposed method. The likelihood function is further exponentially weighted to increase the weight of particles. Simulation results show that compared with the existing methods, this algorithm has better tracking performance.

引用

页码：3267 / 3271

页数：5

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