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

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...

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Veröffentlicht in:EURASIP journal on advances in signal processing Jg. 2021; H. 1; S. 1 - 20
Hauptverfasser: Zhao, Di, Tan, Weijie, Deng, Zhongliang, Li, Gang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cham Springer International Publishing 30.07.2021
Springer
Springer Nature B.V
SpringerOpen
Schlagworte:
Beamspace transformation
Complexity
Computational geometry
Convex analysis
Convex optimization
Convexity
Covariance matrix
Decomposition
Direction of arrival
Direction-of-arrival estimation
Domains
Engineering
Integrated Space-Ground-Sea Wireless Networks for B5G
Linear arrays
Low complexity
Mathematical analysis
Methods
Music
Noise
Optimization
Quantum Information Technology
Signal processing
Signal to noise ratio
Signal,Image and Speech Processing
Sparsity
Spintronics
Uniform circular array
Vector processing (computers)
Vectors (mathematics)
China
Direction-of-arrival estimation
Low complexity
Beamspace transformation
Uniform circular array
Convex optimization
ISSN:1687-6180, 1687-6172, 1687-6180
Online-Zugang:Volltext
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Zusammenfassung: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.
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ISSN:1687-6180
1687-6172
1687-6180
DOI:10.1186/s13634-021-00770-2