FENG Mingyue, HE Minghao, HAN Jun, CHEN Changxiao. 2-D DOA Estimation Using Off-Grid Sparse Learning via Iterative Minimization with L-Parallel Coprime Array[J]. Chinese Journal of Electronics, 2018, 27(6): 1322-1328. doi: 10.1049/cje.2017.11.002
Citation: FENG Mingyue, HE Minghao, HAN Jun, CHEN Changxiao. 2-D DOA Estimation Using Off-Grid Sparse Learning via Iterative Minimization with L-Parallel Coprime Array[J]. Chinese Journal of Electronics, 2018, 27(6): 1322-1328. doi: 10.1049/cje.2017.11.002

2-D DOA Estimation Using Off-Grid Sparse Learning via Iterative Minimization with L-Parallel Coprime Array

doi: 10.1049/cje.2017.11.002
Funds:  This work is supported by the National Natural Science Foundation of China (No.61401504), Natural Foundation of Hubei Province (No.2016CFB288), and the Military Plan of Scientific Research Project (No.2015XXX).
  • Received Date: 2017-05-03
  • Rev Recd Date: 2017-07-05
  • Publish Date: 2018-11-10
  • An L-parallel coprime array is designed and an Off-grid sparse learning via iterative minimization (OGSLIM) algorithm is proposed in order to improve the performance of Two-dimensional direction-of-arrival (2-D DOA) estimation. The L-parallel coprime array consists of two parts, one is a parallel coprime array and the other one is a linear coprime array perpendicular to the parallel coprime array. The OGSLIM algorithm is based on sparse Bayesian framework and can learn the off-grid parameter. Theory analysis and simulation results demonstrate that 2-D DOA estimation using OGSLIM algorithm with L-parallel coprime array can lead to higher estimation accuracy and resolution, it also fits to the underdetermined signals and correlated signals.
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