Low complexity sparse beamspace DOA estimation via single measurement vectors for uniform circular array

EURASIP Journal on Advances in Signal Processing

Weijie Tan¹  Gang Li²  Zhongliang Deng³  Di Zhao⁴ 

[1] State Key Laboratory of Public Big Data, Guizhou Big Data Academy, Guizhou University, 550025, Guiyang, China;The 54th Research Institute of CETC, 050081, Shijiazhuang, China;Wireless Network Positioning and Communication Integration Research Center, School of Electronic Engineering, Beijing University of Posts and Telecommunications, 100876, Beijing, China;Wireless Network Positioning and Communication Integration Research Center, School of Electronic Engineering, Beijing University of Posts and Telecommunications, 100876, Beijing, China;The 54th Research Institute of CETC, 050081, Shijiazhuang, China;

关键词: Direction-of-arrival estimation;    Uniform circular array;    Low complexity;    Beamspace transformation;    Convex optimization;   

DOI  :  10.1186/s13634-021-00770-2

来源: Springer

【 摘 要 】

In this paper, we present a low complexity sparse beamspace direction-of-arrival (DOA) estimation method for uniform circular array (UCA). In the proposed method, we firstly use the beamspace transformation (BT) to transform the signal model of UCA in element-space domain to that of virtual uniform linear array (ULA) in beamspace domain. Subsequently, by applying the vectoring operator on the virtual ULA-like array signal model, a novel dimension-reduction sparse beamspace signal model is derived based on Khatri-Rao (KR) product, the observation data of which is represented by the single measurement vectors (SMVs) via vectorization of sparse covariance matrix. And then, the DOA estimation is formulated as a convex optimization problem by following the concept of a sparse-signal-representation (SSR) of the SMVs. Finally, simulations are carried out to validate the effectiveness of the proposed method. The results show that without knowledge of the number of signals, the proposed method not only has higher DOA resolution than the subspace-based methods in low signal-to-noise ratio (SNR), but also has far lower computational complexity than other sparse-like DOA estimation methods.

【 授权许可】

CC BY