Pseudo Noise Subspace Based DOA Estimation for Unfolded Coprime Linear Arrays

Autor:	Xiaoqing Yang, Lu Cao, Xiangdong Huang, Wei Lu
Rok vydání:	2021
Předmět:	Noise Sparse array Coprime integers Sampling (signal processing) Control and Systems Engineering Computer science Nyquist–Shannon sampling theorem Estimator Electrical and Electronic Engineering Algorithm Image resolution Subspace topology
Zdroj:	IEEE Wireless Communications Letters. 10:2335-2339
ISSN:	2162-2345 2162-2337
Popis:	To achieve a better tradeoff between the direction-of-arrival (DOA) estimation resolution and complexity compared to the existing sparse array based estimators, this letter proposes a pseudo noise subspace based DOA estimator for an unfolded coprime linear array (UCLA). On one hand, by unfolding the two subarrays of a general coprime linear array (CLA), the UCLA acquires a larger aperture, giving rise to a higher angular resolution. On the other hand, a mapping relationship is explored to convert the original noise subspace into the pseudo noise subspace (in the Nyquist spatial sampling sense), from which the DOAs can be accurately estimated by root-MUSIC method instead of exhaustive peak searching, thereby reducing the complexity. Numerical results also confirm this superiority over other sparse array based estimators.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::80f8345d7b463952994cb351ee140dfe https://doi.org/10.1109/lwc.2021.3091989 Zobrazit plný text záznamu