多输入多输出(Multiple-Input Multiple-Output,MIMO)雷达在阵元故障时虚拟阵列输出数据矩阵会出现大量的整行数据丢失,由于阵列接收数据矩阵的不完整而导致对波达方向(Direction of Arrival,DOA)的估计性能恶化。大多数低秩矩阵填充算...多输入多输出(Multiple-Input Multiple-Output,MIMO)雷达在阵元故障时虚拟阵列输出数据矩阵会出现大量的整行数据丢失,由于阵列接收数据矩阵的不完整而导致对波达方向(Direction of Arrival,DOA)的估计性能恶化。大多数低秩矩阵填充算法要求缺失数据随机分布于不完整的矩阵中,无法适用于整行缺失数据的恢复问题。为此,提出了一种基于低秩块Hankel矩阵正则化的阵元故障MIMO雷达DOA估计方法。首先,通过奇异值分解(Singular Value Decomposition,SVD)降低虚拟阵列输出矩阵的维度,以减少计算复杂度。然后,对降维数据矩阵建立基于块Hankel矩阵正则化的低秩矩阵填充模型,在该模型中将MIMO雷达降维数据矩阵排列成块Hankel矩阵并施加Schatten-p范数作为正则项。最后,结合交替方向乘子法(Alternate Direction Multiplier Method,ADMM)求解该模型,获得完整的MIMO雷达降维数据矩阵。仿真结果表明,所提方法能够有效恢复降维数据矩阵中的整行数据缺失,具有较高的DOA估计精度和实时性,在阵元故障率低于50.0%时DOA估计精度优于现有方法。展开更多
A number of previous papers have studied the problem of recovering low-rank matrices with noise, further combining the noisy and perturbed cases, we propose a nonconvex Schatten p-norm minimization method to deal with...A number of previous papers have studied the problem of recovering low-rank matrices with noise, further combining the noisy and perturbed cases, we propose a nonconvex Schatten p-norm minimization method to deal with the recovery of fully perturbed low-rank matrices. By utilizing the p-null space property (p-NSP) and the p-restricted isometry property (p-RIP) of the matrix, sufficient conditions to ensure that the stable and accurate reconstruction for low-rank matrix in the case of full perturbation are derived, and two upper bound recovery error estimation ns are given. These estimations are characterized by two vital aspects, one involving the best r-approximation error and the other concerning the overall noise. Specifically, this paper obtains two new error upper bounds based on the fact that p-RIP and p-NSP are able to recover accurately and stably low-rank matrix, and to some extent improve the conditions corresponding to RIP.展开更多
与均匀阵列相比,稀疏阵列可以使天线阵列成本降低,减少数据处理,同时带来更大的阵列孔径提高信号解析能力,在信号处理中有着广泛的应用。但是由于其排布的不规则性,计算量较大,二维面阵合成协方差矩阵存在空洞,对角度估计的准确性造成...与均匀阵列相比,稀疏阵列可以使天线阵列成本降低,减少数据处理,同时带来更大的阵列孔径提高信号解析能力,在信号处理中有着广泛的应用。但是由于其排布的不规则性,计算量较大,二维面阵合成协方差矩阵存在空洞,对角度估计的准确性造成负面影响,增强了系统对噪声的敏感度。为了克服这些问题,本文提出了一种新的角度估计方法,采用截断核范数以降低噪声的影响,并通过ℓ_(p)范数优化提升信号的稀疏表示,利用交替方向乘子法(Alternating Direction Method of Multipliers,ADMM)算法构造子问题恢复出完整的阵列信号。随后采用子阵划分技术和基于最小二乘的传播算子模型(Propagator Method,PM)对恢复的信号处理,精确估计信号源的方位和俯仰角。仿真结果表明,所提出的角度估计算法在角度精度和时间复杂度方面具有优越性。展开更多
文摘多输入多输出(Multiple-Input Multiple-Output,MIMO)雷达在阵元故障时虚拟阵列输出数据矩阵会出现大量的整行数据丢失,由于阵列接收数据矩阵的不完整而导致对波达方向(Direction of Arrival,DOA)的估计性能恶化。大多数低秩矩阵填充算法要求缺失数据随机分布于不完整的矩阵中,无法适用于整行缺失数据的恢复问题。为此,提出了一种基于低秩块Hankel矩阵正则化的阵元故障MIMO雷达DOA估计方法。首先,通过奇异值分解(Singular Value Decomposition,SVD)降低虚拟阵列输出矩阵的维度,以减少计算复杂度。然后,对降维数据矩阵建立基于块Hankel矩阵正则化的低秩矩阵填充模型,在该模型中将MIMO雷达降维数据矩阵排列成块Hankel矩阵并施加Schatten-p范数作为正则项。最后,结合交替方向乘子法(Alternate Direction Multiplier Method,ADMM)求解该模型,获得完整的MIMO雷达降维数据矩阵。仿真结果表明,所提方法能够有效恢复降维数据矩阵中的整行数据缺失,具有较高的DOA估计精度和实时性,在阵元故障率低于50.0%时DOA估计精度优于现有方法。
文摘A number of previous papers have studied the problem of recovering low-rank matrices with noise, further combining the noisy and perturbed cases, we propose a nonconvex Schatten p-norm minimization method to deal with the recovery of fully perturbed low-rank matrices. By utilizing the p-null space property (p-NSP) and the p-restricted isometry property (p-RIP) of the matrix, sufficient conditions to ensure that the stable and accurate reconstruction for low-rank matrix in the case of full perturbation are derived, and two upper bound recovery error estimation ns are given. These estimations are characterized by two vital aspects, one involving the best r-approximation error and the other concerning the overall noise. Specifically, this paper obtains two new error upper bounds based on the fact that p-RIP and p-NSP are able to recover accurately and stably low-rank matrix, and to some extent improve the conditions corresponding to RIP.
文摘与均匀阵列相比,稀疏阵列可以使天线阵列成本降低,减少数据处理,同时带来更大的阵列孔径提高信号解析能力,在信号处理中有着广泛的应用。但是由于其排布的不规则性,计算量较大,二维面阵合成协方差矩阵存在空洞,对角度估计的准确性造成负面影响,增强了系统对噪声的敏感度。为了克服这些问题,本文提出了一种新的角度估计方法,采用截断核范数以降低噪声的影响,并通过ℓ_(p)范数优化提升信号的稀疏表示,利用交替方向乘子法(Alternating Direction Method of Multipliers,ADMM)算法构造子问题恢复出完整的阵列信号。随后采用子阵划分技术和基于最小二乘的传播算子模型(Propagator Method,PM)对恢复的信号处理,精确估计信号源的方位和俯仰角。仿真结果表明,所提出的角度估计算法在角度精度和时间复杂度方面具有优越性。
