针对差分共阵存在空洞,难以充分利用互质阵列信息的问题,提出了一种互质阵列的相干信号波束域波达方向(direction of arrival,DOA)估计方法。该方法首先利用差分集合构建虚拟阵列,在空洞位置补零,将传统互质阵列转化为虚拟均匀线阵;接着...针对差分共阵存在空洞,难以充分利用互质阵列信息的问题,提出了一种互质阵列的相干信号波束域波达方向(direction of arrival,DOA)估计方法。该方法首先利用差分集合构建虚拟阵列,在空洞位置补零,将传统互质阵列转化为虚拟均匀线阵;接着,将虚拟均匀线阵划分为若干个相互重叠的子阵,通过波束形成将阵列接收数据从阵元域转换至波束域;然后,将多个波束方向上的输出协方差矩阵相加求和,构造波束域矩阵,实现了信号的去相关,提高了信噪比;最后,将其与子空间类方法结合实现DOA估计。仿真实验和湖试数据处理结果表明,所提方法具有较好的方位估计性能,在实际应用中具有良好的鲁棒性,验证了其有效性和适用性。展开更多
针对复杂电磁环境中因低信噪比、快拍采样数据不足、非高斯杂波干扰及多径传播效应等导致的波达方向(Direction of Arrival,DOA)估计性能退化问题,本文提出了一种基于降维高阶累积量与低秩矩阵重构的多源相干信号的DOA估计算法。首先通...针对复杂电磁环境中因低信噪比、快拍采样数据不足、非高斯杂波干扰及多径传播效应等导致的波达方向(Direction of Arrival,DOA)估计性能退化问题,本文提出了一种基于降维高阶累积量与低秩矩阵重构的多源相干信号的DOA估计算法。首先通过构建四阶累积量矩阵扩展阵列孔径,抑制高斯噪声,提升欠定条件下的信号自由度;随后采用高效的降维策略,显著降低计算复杂度;最后通过交替方向乘子法求解低秩约束下的Toeplitz协方差矩阵重构问题,实现了复杂环境下多源相干信号的高精度定位。实验结果表明,本算法在低信噪比及少快拍数下对多源相干信号依然有出色的估计性能,兼具高精度和强抗干扰特性,有良好的工程实用价值。展开更多
在相干信号波达方向(direction of arrival,DOA)估计中,当阵列接收到的相干信号处于低信噪比时,DOA估计性能会大大降低。针对该问题,提出一种增强的时空平滑(enhanced spatio-temporal smoothing,ESTS)算法,在使用时空相关矩阵重构接收...在相干信号波达方向(direction of arrival,DOA)估计中,当阵列接收到的相干信号处于低信噪比时,DOA估计性能会大大降低。针对该问题,提出一种增强的时空平滑(enhanced spatio-temporal smoothing,ESTS)算法,在使用时空相关矩阵重构接收数据矩阵的时空平滑(spatio-temporal smoothing,STS)方法的基础上进行了改进。首先对子阵列时空相关矩阵进行平方预处理,然后通过充分利用子阵列时空相关矩阵的协方差和互协方差信息解相干,提高了相干信号的分辨率以及对噪声扰动的鲁棒性。理论分析和统计结果均表明,与其他空间平滑类解相干方法相比,该方法提高了在低信噪比、少快拍数、小角度分离情况下的相干信号DOA估计的去相关性能。展开更多
针对幅相误差条件下多输入多输出(Multiple-Input Multiple-Output,MIMO)雷达对相干信号波达方向(Direction of Arrival,DOA)估计性能下降的问题,提出了一种基于功率自适应子阵平滑与噪声子空间迭代优化的改进多重信号分类(Multiple Sig...针对幅相误差条件下多输入多输出(Multiple-Input Multiple-Output,MIMO)雷达对相干信号波达方向(Direction of Arrival,DOA)估计性能下降的问题,提出了一种基于功率自适应子阵平滑与噪声子空间迭代优化的改进多重信号分类(Multiple Signal Classification,MUSIC)算法。该方法通过两阶段优化策略提升传统算法鲁棒性。首先,设计功率自适应子阵平滑机制,依据子阵信号强度动态加权协方差矩阵,抑制幅相误差引起的子阵间能量失配。其次,引入噪声子空间投影矩阵迭代优化算法,利用随机误差补偿技术修正噪声空间畸变,降低误差对正交性判据的干扰。最后,通过特征分解优化的投影矩阵得到新噪声子空间,并结合MUSIC算法实现DOA估计。仿真结果表明,在典型幅相误差场景下,所提算法相较于传统算法,DOA估计均方根误差降低25%,相干信号分辨成功率提升30%。展开更多
波达方向(direction of arrival,DOA)估计是阵列信号处理的关键技术。基于可编程超表面的DOA估计方法能够在单通道接收架构下有效降低系统硬件成本与实现复杂度,但现有方法通常依赖空间角度离散化建模,其性能易受网格失配问题的制约。...波达方向(direction of arrival,DOA)估计是阵列信号处理的关键技术。基于可编程超表面的DOA估计方法能够在单通道接收架构下有效降低系统硬件成本与实现复杂度,但现有方法通常依赖空间角度离散化建模,其性能易受网格失配问题的制约。针对上述问题,本文提出了基于联合稀疏恢复的可编程超表面离网格DOA估计方法。首先,基于一阶泰勒展开构建同时包含网格项与导数项的联合过完备字典,建立联合DOA估计模型,从而实现对离网格误差的显式建模与校正。进一步地,为降低联合模型在二维场景下的计算与存储开销,提出了基于Kronecker分解的模型简化方法,将高维运算转化为低维矩阵运算。在此基础上,分别设计了联合正交匹配追踪(joint orthogonal matching pursuit,JOMP)算法及其基于Kronecker分解的高效实现算法——KJOMP用于模型求解。仿真与实测结果表明,对于单目标与多目标,不同超表面规模、编码次数及信噪比条件下,所提JOMP算法在一维和二维DOA估计精度方面均表现出优于传统网格化方法的估计性能;KJOMP算法在保持与JOMP算法估计精度基本一致的同时,运行时间和空间复杂度大幅下降。本文方法在雷达、物联网、6G通信、定位导航及无人机等应用场景中具有良好的应用潜力。展开更多
As is well known,mutual coupling between array elements has a significant negative impact on direction of arrival(DOA)estimation.To achieve DOA estimation under unknown mutual coupling,this paper proposes a low comput...As is well known,mutual coupling between array elements has a significant negative impact on direction of arrival(DOA)estimation.To achieve DOA estimation under unknown mutual coupling,this paper proposes a low computational complexity Newton-like method.Firstly,a block sparse model based on the signal subspace is established,and the Lagrangian function is established according to the block sparse model.Secondly,since the Hessian matrix of the Lagrangian function cannot always ensure positive definiteness and the computational complexity of the inverse matrix of the Hessian matrix is enormous,the Newton method is no longer applicable.Therefore,this paper proposes a Newton-like method to achieve DOA estimation under mutual coupling and reduce the computational complexity by matrix inversion lemma.Finally,compared with existing methods of DOA estimation under array mutual coupling,the simulation results validate the effectiveness of the proposed method.展开更多
Signal of opportunity(SOP)has become an attractive source of navigation in the absence of global navigation satellite system(GNSS).However,in some typical GNSS-limited environments,such as deep urban canyons,the SOP p...Signal of opportunity(SOP)has become an attractive source of navigation in the absence of global navigation satellite system(GNSS).However,in some typical GNSS-limited environments,such as deep urban canyons,the SOP positioning is challenged by low signal-to-noise ratio(SNR),rapidly time-varying channels,and gain/phase uncertainties.To overcome these challenges,we propose a sparse direction of arrival(DOA)estimation method specifically designed for SOP positioning.Under the conditions of low SNR and limited number of snapshots,we conduct detailed theoretical derivations and simulation experiments to analyze the negative impact of gain and phase uncertainties on sparse DOA estimation.The analysis indicates that these uncertainties can lead to an increase in the number or height of spurious peaks in the DOA spatial spectrum,thereby significantly reducing the accuracy of DOA estimation.To address this issue,we propose a non-iterative sparse DOA estimation method that combines blind source separation(BSS)and singular value decomposition(SVD)techniques.The BSS algorithm accurately determines the number of SOPs using a single sensor,effectively eliminating the impact of gain and phase uncertainties between sensors.Once the number of SOPs is obtained,we can introduce the SVD algorithm to further enhance the DOA estimation performance under low SNR conditions.Simulation results validate the effectiveness of the proposed method in DOA estimation,showcasing its excellent robustness and self-calibration characteristics while maintaining reasonable computational costs.The introduction of this method provides a new solution to the navigation and positioning problem in GNSS-denied environments.展开更多
针对水下目标方位(Direction of Arrival,DOA)估计准确性实时性的要求,理论分析了互质阵列模型、压缩感知DOA估计的原理,设计实现了基于FPGA的互质阵列压缩感知算法DOA估计系统。首先介绍了系统开发环境,包括平台选择、开发流程等;其次...针对水下目标方位(Direction of Arrival,DOA)估计准确性实时性的要求,理论分析了互质阵列模型、压缩感知DOA估计的原理,设计实现了基于FPGA的互质阵列压缩感知算法DOA估计系统。首先介绍了系统开发环境,包括平台选择、开发流程等;其次,介绍了硬件系统的整体框架,重点说明了PS与PL之间的数据传递流程和硬件各模块实现过程,并仿真验证了该系统的正确性。在Xilinx FPGA平台上进行了湖试数据的处理,完成了数据运算参数的统计收集,验证了DOA估计的有效性,并计算了运算耗时。结果表明,所设计的系统能够正确完成DOA估计并满足实时性要求。展开更多
摘要针对差分共阵存在空洞,难以充分利用互质阵列信息的问题,提出了一种互质阵列的相干信号波束域波达方向(direction of arrival,DOA)估计方法。该方法首先利用差分集合构建虚拟阵列,在空洞位置补零,将传统互质阵列转化为虚拟均匀线阵;接着,将虚拟均匀线阵划分为若干个相互重叠的子阵,通过波束形成将阵列接收数据从阵元域转换至波束域;然后,将多个波束方向上的输出协方差矩阵相加求和,构造波束域矩阵,实现了信号的去相关,提高了信噪比;最后,将其与子空间类方法结合实现DOA估计。仿真实验和湖试数据处理结果表明,所提方法具有较好的方位估计性能,在实际应用中具有良好的鲁棒性,验证了其有效性和适用性。
摘要针对复杂电磁环境中因低信噪比、快拍采样数据不足、非高斯杂波干扰及多径传播效应等导致的波达方向(Direction of Arrival,DOA)估计性能退化问题,本文提出了一种基于降维高阶累积量与低秩矩阵重构的多源相干信号的DOA估计算法。首先通过构建四阶累积量矩阵扩展阵列孔径,抑制高斯噪声,提升欠定条件下的信号自由度;随后采用高效的降维策略,显著降低计算复杂度;最后通过交替方向乘子法求解低秩约束下的Toeplitz协方差矩阵重构问题,实现了复杂环境下多源相干信号的高精度定位。实验结果表明,本算法在低信噪比及少快拍数下对多源相干信号依然有出色的估计性能,兼具高精度和强抗干扰特性,有良好的工程实用价值。
摘要针对幅相误差条件下多输入多输出(Multiple-Input Multiple-Output,MIMO)雷达对相干信号波达方向(Direction of Arrival,DOA)估计性能下降的问题,提出了一种基于功率自适应子阵平滑与噪声子空间迭代优化的改进多重信号分类(Multiple Signal Classification,MUSIC)算法。该方法通过两阶段优化策略提升传统算法鲁棒性。首先,设计功率自适应子阵平滑机制,依据子阵信号强度动态加权协方差矩阵,抑制幅相误差引起的子阵间能量失配。其次,引入噪声子空间投影矩阵迭代优化算法,利用随机误差补偿技术修正噪声空间畸变,降低误差对正交性判据的干扰。最后,通过特征分解优化的投影矩阵得到新噪声子空间,并结合MUSIC算法实现DOA估计。仿真结果表明,在典型幅相误差场景下,所提算法相较于传统算法,DOA估计均方根误差降低25%,相干信号分辨成功率提升30%。
基金supported by the National Natural Science Foundation of China(6207114462171150)Taishan Scholar Special Funding Project of Shandong Province(tsqn202211087)。
摘要As is well known,mutual coupling between array elements has a significant negative impact on direction of arrival(DOA)estimation.To achieve DOA estimation under unknown mutual coupling,this paper proposes a low computational complexity Newton-like method.Firstly,a block sparse model based on the signal subspace is established,and the Lagrangian function is established according to the block sparse model.Secondly,since the Hessian matrix of the Lagrangian function cannot always ensure positive definiteness and the computational complexity of the inverse matrix of the Hessian matrix is enormous,the Newton method is no longer applicable.Therefore,this paper proposes a Newton-like method to achieve DOA estimation under mutual coupling and reduce the computational complexity by matrix inversion lemma.Finally,compared with existing methods of DOA estimation under array mutual coupling,the simulation results validate the effectiveness of the proposed method.
摘要Signal of opportunity(SOP)has become an attractive source of navigation in the absence of global navigation satellite system(GNSS).However,in some typical GNSS-limited environments,such as deep urban canyons,the SOP positioning is challenged by low signal-to-noise ratio(SNR),rapidly time-varying channels,and gain/phase uncertainties.To overcome these challenges,we propose a sparse direction of arrival(DOA)estimation method specifically designed for SOP positioning.Under the conditions of low SNR and limited number of snapshots,we conduct detailed theoretical derivations and simulation experiments to analyze the negative impact of gain and phase uncertainties on sparse DOA estimation.The analysis indicates that these uncertainties can lead to an increase in the number or height of spurious peaks in the DOA spatial spectrum,thereby significantly reducing the accuracy of DOA estimation.To address this issue,we propose a non-iterative sparse DOA estimation method that combines blind source separation(BSS)and singular value decomposition(SVD)techniques.The BSS algorithm accurately determines the number of SOPs using a single sensor,effectively eliminating the impact of gain and phase uncertainties between sensors.Once the number of SOPs is obtained,we can introduce the SVD algorithm to further enhance the DOA estimation performance under low SNR conditions.Simulation results validate the effectiveness of the proposed method in DOA estimation,showcasing its excellent robustness and self-calibration characteristics while maintaining reasonable computational costs.The introduction of this method provides a new solution to the navigation and positioning problem in GNSS-denied environments.
摘要针对水下目标方位(Direction of Arrival,DOA)估计准确性实时性的要求,理论分析了互质阵列模型、压缩感知DOA估计的原理,设计实现了基于FPGA的互质阵列压缩感知算法DOA估计系统。首先介绍了系统开发环境,包括平台选择、开发流程等;其次,介绍了硬件系统的整体框架,重点说明了PS与PL之间的数据传递流程和硬件各模块实现过程,并仿真验证了该系统的正确性。在Xilinx FPGA平台上进行了湖试数据的处理,完成了数据运算参数的统计收集,验证了DOA估计的有效性,并计算了运算耗时。结果表明,所设计的系统能够正确完成DOA估计并满足实时性要求。