针对现有测向系统多信号适应能力弱、测向精度低的问题,提出一种基于数据驱动的高精度阵列测向新方法。该方法提取单信号入射时的输入特征向量,基于卷积神经网络构建单信号测向网络框架。利用信号的独立性,将多信号测向问题转化为单信...针对现有测向系统多信号适应能力弱、测向精度低的问题,提出一种基于数据驱动的高精度阵列测向新方法。该方法提取单信号入射时的输入特征向量,基于卷积神经网络构建单信号测向网络框架。利用信号的独立性,将多信号测向问题转化为单信号测向问题,在单信号训练网络的基础上实现多信号来波方向估计。仿真实验与理论分析结果表明,该方法有效减少了输入特征维数和网络训练样本数目,在多信号同时入射及阵列互耦效应条件下均获得了高精度的到达方向(Direction of Arrival,DOA)估计的测向结果。展开更多
The direction of arrival(DOA) estimation problem in the presence of sensor location errors is studied and an algorithm based on space alternating generalized expectation-maximization(SAGE) is presented. First, the nar...The direction of arrival(DOA) estimation problem in the presence of sensor location errors is studied and an algorithm based on space alternating generalized expectation-maximization(SAGE) is presented. First, the narrowband case is considered.Based on the small perturbation assumption, this paper proposes an augmentation scheme so as to estimate DOA and perturbation parameters. The E-step and M-step of the SAGE algorithm in this case are derived. Then, the algorithm is extended to the wideband case. The wideband SAGE algorithm is derived in frequency domain by jointing all frequency bins. Simulation results show that the algorithm achieves good convergence and high parameter estimation precision.展开更多
In this paper, a novel algorithm is presented for direction of arrival（DOA） estimation and array self-calibration in the presence of unknown mutual coupling. In order to highlight the relationship between the array ...In this paper, a novel algorithm is presented for direction of arrival（DOA） estimation and array self-calibration in the presence of unknown mutual coupling. In order to highlight the relationship between the array output and mutual coupling coefficients, we present a novel model of the array output with the unknown mutual coupling coefficients. Based on this model, we use the space alternating generalized expectation-maximization（SAGE） algorithm to jointly estimate the DOA parameters and the mutual coupling coefficients. Unlike many existing counterparts, our method requires neither calibration sources nor initial calibration information. At the same time,our proposed method inherits the characteristics of good convergence and high estimation precision of the SAGE algorithm. By numerical experiments we demonstrate that our proposed method outperforms the existing method for DOA estimation and mutual coupling calibration.展开更多
文摘针对现有测向系统多信号适应能力弱、测向精度低的问题,提出一种基于数据驱动的高精度阵列测向新方法。该方法提取单信号入射时的输入特征向量,基于卷积神经网络构建单信号测向网络框架。利用信号的独立性,将多信号测向问题转化为单信号测向问题,在单信号训练网络的基础上实现多信号来波方向估计。仿真实验与理论分析结果表明,该方法有效减少了输入特征维数和网络训练样本数目,在多信号同时入射及阵列互耦效应条件下均获得了高精度的到达方向(Direction of Arrival,DOA)估计的测向结果。
文摘The direction of arrival(DOA) estimation problem in the presence of sensor location errors is studied and an algorithm based on space alternating generalized expectation-maximization(SAGE) is presented. First, the narrowband case is considered.Based on the small perturbation assumption, this paper proposes an augmentation scheme so as to estimate DOA and perturbation parameters. The E-step and M-step of the SAGE algorithm in this case are derived. Then, the algorithm is extended to the wideband case. The wideband SAGE algorithm is derived in frequency domain by jointing all frequency bins. Simulation results show that the algorithm achieves good convergence and high parameter estimation precision.
基金supported by the National Natural Science Foundation of China (No. 61302141)
文摘In this paper, a novel algorithm is presented for direction of arrival（DOA） estimation and array self-calibration in the presence of unknown mutual coupling. In order to highlight the relationship between the array output and mutual coupling coefficients, we present a novel model of the array output with the unknown mutual coupling coefficients. Based on this model, we use the space alternating generalized expectation-maximization（SAGE） algorithm to jointly estimate the DOA parameters and the mutual coupling coefficients. Unlike many existing counterparts, our method requires neither calibration sources nor initial calibration information. At the same time,our proposed method inherits the characteristics of good convergence and high estimation precision of the SAGE algorithm. By numerical experiments we demonstrate that our proposed method outperforms the existing method for DOA estimation and mutual coupling calibration.
