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 problem of two-dimensional direction finding is approached by using a multi-layer Lshaped array. The proposed method is based on two sequential sparse representations,fulfilling respectively the estimation of elev...The problem of two-dimensional direction finding is approached by using a multi-layer Lshaped array. The proposed method is based on two sequential sparse representations,fulfilling respectively the estimation of elevation angles,and azimuth angles. For the estimation of elevation angles,the weighted sub-array smoothing technique for perfect data decorrelation is used to produce a covariance vector suitable for exact sparse representation,related only to the elevation angles. The estimates of elevation angles are then obtained by sparse restoration associated with this elevation angle dependent covariance vector. The estimates of elevation angles are further incorporated with weighted sub-array smoothing to yield a second covariance vector for precise sparse representation related to both elevation angles,and azimuth angles. The estimates of azimuth angles,automatically paired with the estimates of elevation angles,are finally obtained by sparse restoration associated with this latter elevation-azimuth angle related covariance vector. Simulation results are included to illustrate the performance of the proposed method.展开更多
针对分布式多输入多输出(multi-input multi-output,MIMO)雷达测向中存在的数据信息提取不充分、运算量偏大等问题,开展了基于广义奇异值分解(generalized singular value decomposition,GSVD)的测向算法研究,以提高低信噪比条件下的角...针对分布式多输入多输出(multi-input multi-output,MIMO)雷达测向中存在的数据信息提取不充分、运算量偏大等问题,开展了基于广义奇异值分解(generalized singular value decomposition,GSVD)的测向算法研究,以提高低信噪比条件下的角度估计性能。首先,建立了分布式阵列MIMO雷达回波信号的统一化表征模型;其次,将分布式MIMO雷达系统接收阵列数据的多线程GSVD问题转换为一个联合优化问题,运用交替最小二乘(alternating least squares,ALS)技术实现阵列信号流行矩阵的拟合,并引入子空间类算法实现目标角度联合估计;最后,对优化问题增加l1范数约束,避免了每次迭代中进行的奇异值分解运算,降低了算法运算量。仿真实验从角度联合估计、均方误差、运算时间等方面验证了所提算法的有效性。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。
基金Supported by the National Natural Science Foundation of China(61331019,61490691)
文摘The problem of two-dimensional direction finding is approached by using a multi-layer Lshaped array. The proposed method is based on two sequential sparse representations,fulfilling respectively the estimation of elevation angles,and azimuth angles. For the estimation of elevation angles,the weighted sub-array smoothing technique for perfect data decorrelation is used to produce a covariance vector suitable for exact sparse representation,related only to the elevation angles. The estimates of elevation angles are then obtained by sparse restoration associated with this elevation angle dependent covariance vector. The estimates of elevation angles are further incorporated with weighted sub-array smoothing to yield a second covariance vector for precise sparse representation related to both elevation angles,and azimuth angles. The estimates of azimuth angles,automatically paired with the estimates of elevation angles,are finally obtained by sparse restoration associated with this latter elevation-azimuth angle related covariance vector. Simulation results are included to illustrate the performance of the proposed method.
文摘针对分布式多输入多输出(multi-input multi-output,MIMO)雷达测向中存在的数据信息提取不充分、运算量偏大等问题,开展了基于广义奇异值分解(generalized singular value decomposition,GSVD)的测向算法研究,以提高低信噪比条件下的角度估计性能。首先,建立了分布式阵列MIMO雷达回波信号的统一化表征模型;其次,将分布式MIMO雷达系统接收阵列数据的多线程GSVD问题转换为一个联合优化问题,运用交替最小二乘(alternating least squares,ALS)技术实现阵列信号流行矩阵的拟合,并引入子空间类算法实现目标角度联合估计;最后,对优化问题增加l1范数约束,避免了每次迭代中进行的奇异值分解运算,降低了算法运算量。仿真实验从角度联合估计、均方误差、运算时间等方面验证了所提算法的有效性。
