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

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