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

Di Zhao,Weijie Tan,Gang Li,Zhongliang Deng

doi:10.1186/s13634-021-00770-2

Abstract

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.

Highlights

In the past decades, direction-of-arrival (DOA) estimation of propagating plane waves for uniform circular array (UCA) has been widely used in various fields, such as communication, radar, sonar, radio astronomy and so on [1]
We propose a low complexity sparse beamspace DOA estimation for UCA by vectorizing the array covariance vectors, called BS- 1-SRSMVS, which exploits the methodology combining the beamspace transformation (BT) technique and the SSR model of single measurement vectors (SMVs) in beamspace domain
2 Related works Here we focus on some DOA estimation methods based on sparse signal representation [8,9,10, 15]

Summary

Introduction

Direction-of-arrival (DOA) estimation of propagating plane waves for uniform circular array (UCA) has been widely used in various fields, such as communication, radar, sonar, radio astronomy and so on [1]. In [9], a low complexity sparse covariance-based DOA estimation method called LC-SRACV is proposed, which uses the Khatri-Rao (KR) product in SSR framework to recover array covariance vectors of only one single measurement vector. These methods mentioned above are all manipulated in element-space domain. We propose a low complexity sparse beamspace DOA estimation for UCA by vectorizing the array covariance vectors, called BS- 1-SRSMVS, which exploits the methodology combining the BT technique and the SSR model of single measurement vectors (SMVs) in beamspace domain.

Singular vectors of observation data matrix

Covariance matrix vectors of signal space

Procedures

Results and discussion

Performance evaluation

Conclusion