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
针对在现场可编程门阵列(Field Programmable Gate Array,FPGA)上实现基于极化敏感阵列的多重信号分类(Multiple Signal Classification,MUSIC)算法进行二维波达方向(Direction of Arrival,DOA)和二维极化参数联合估计时,硬件资源占用...针对在现场可编程门阵列(Field Programmable Gate Array,FPGA)上实现基于极化敏感阵列的多重信号分类(Multiple Signal Classification,MUSIC)算法进行二维波达方向(Direction of Arrival,DOA)和二维极化参数联合估计时,硬件资源占用大、运行时间长的问题,提出了一种基于极化MUSIC算法的四维参数联合估计FPGA实现架构。该架构包括信号协方差矩阵计算模块、Jacobi旋转模块、噪声子空间提取模块、两级空间谱搜索模块和极化参数计算模块。Jacobi旋转模块被拆分为多个可复用模块,并采用查找表模块生成旋转矩阵。一级空间谱搜索模块通过二维DOA搜索初步确定信源的角度信息。二级空间谱搜索模块根据一级搜索的角度结果确定二级搜索区域各点的极化信息,并计算该区域的四维空间谱,区域内最小值对应的四维参数信息即为最终估计的信源方向角、俯仰角、极化辅助角和极化相位角。仿真结果表明,与传统极化MUSIC算法的四维搜索算法相比,该架构避免了大量四维空间谱计算,同时保证了四维参数估计的精度,显著减少了运行时间和硬件资源消耗。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
Most of the existing direction of arrival(DOA)estimation algorithms are applied under the assumption that the array manifold is ideal.In practical engineering applications,the existence of non-ideal conditions such as...Most of the existing direction of arrival(DOA)estimation algorithms are applied under the assumption that the array manifold is ideal.In practical engineering applications,the existence of non-ideal conditions such as mutual coupling between array elements,array amplitude and phase errors,and array element position errors leads to defects in the array manifold,which makes the performance of the algorithm decline rapidly or even fail.In order to solve the problem of DOA estimation in the presence of amplitude and phase errors and array element position errors,this paper introduces the first-order Taylor expansion equivalent model of the received signal under the uniform linear array from the Bayesian point of view.In the solution,the amplitude and phase error parameters and the array element position error parameters are regarded as random variables obeying the Gaussian distribution.At the same time,the expectation-maximization algorithm is used to update the probability distribution parameters,and then the two error parameters are solved alternately to obtain more accurate DOA estimation results.Finally,the effectiveness of the proposed algorithm is verified by simulation and experiment.展开更多
Sparse array design has significant implications for improving the accuracy of direction of arrival(DOA)estimation of non-circular(NC)signals.We propose an extended nested array with a filled sensor(ENAFS)based on the...Sparse array design has significant implications for improving the accuracy of direction of arrival(DOA)estimation of non-circular(NC)signals.We propose an extended nested array with a filled sensor(ENAFS)based on the hole-filling strategy.Specifically,we first introduce the improved nested array(INA)and prove its properties.Subsequently,we extend the sum-difference coarray(SDCA)by adding an additional sensor to fill the holes.Thus the larger uniform degrees of freedom(uDOFs)and virtual array aperture(VAA)can be abtained,and the ENAFS is designed.Finally,the simulation results are given to verify the superiority of the proposed ENAFS in terms of DOF,mutual coupling and estimation performance.展开更多
Interference significantly impacts the performance of the Global Navigation Satellite Systems(GNSS),highlighting the need for advanced interference localization technology to bolster anti-interference and defense capa...Interference significantly impacts the performance of the Global Navigation Satellite Systems(GNSS),highlighting the need for advanced interference localization technology to bolster anti-interference and defense capabilities.The Uniform Circular Array(UCA)enables concurrent estimation of the Direction of Arrival(DOA)in both azimuth and elevation.Given the paramount importance of stability and real-time performance in interference localization,this work proposes an innovative approach to reduce the complexity and increase the robustness of the DOA estimation.The proposed method reduces computational complexity by selecting a reduced number of array elements to reconstruct a non-uniform sparse array from a UCA.To ensure DOA estimation accuracy,minimizing the Cramér-Rao Bound(CRB)is the objective,and the Spatial Correlation Coefficient(SCC)is incorporated as a constraint to mitigate side-lobe.The optimization model is a quadratic fractional model,which is solved by Semi-Definite Relaxation(SDR).When the array has perturbations,the mathematical expressions for CRB and SCC are re-derived to enhance the robustness of the reconstructed array.Simulation and hardware experiments validate the effectiveness of the proposed method in estimating interference DOA,showing high robustness and reductions in hardware and computational costs associated with DOA estimation.展开更多
针对集中式框架下波达方向(direction of arrival, DOA)估计存在的计算量大、灵活性不足等问题,提出了基于扩展互质阵列的分布式DOA估计算法,实现以低计算量对目标DOA进行快速估计。首先,在每个子阵采用扩展互质阵布局,通过对局部采样...针对集中式框架下波达方向(direction of arrival, DOA)估计存在的计算量大、灵活性不足等问题,提出了基于扩展互质阵列的分布式DOA估计算法,实现以低计算量对目标DOA进行快速估计。首先,在每个子阵采用扩展互质阵布局,通过对局部采样协方差矩阵向量化构建差分虚拟阵列,识别并提取最长连续虚拟均匀线性阵列(uniform linear array, ULA)以去除空洞;随后,将DOA估计问题表述为基于角度网格的稀疏重构凸优化问题,并在分布式网络中构建基于共识的交替方向乘子法(alternating direction method of multipliers, ADMM)求解框架,使各子阵通过本地计算与融合中心协同恢复全局稀疏解。所提方法适用于大孔径、低成本及实时性要求高的大规模阵列信号处理场景。仿真实验从均方误差、运算时间等方面验证了所提算法的有效性。展开更多
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
To solve the problem of performance degradation caused by insufficient array signal feature representation under limited snapshot conditions,this research proposes a novel deep transfer learning approach specifically ...To solve the problem of performance degradation caused by insufficient array signal feature representation under limited snapshot conditions,this research proposes a novel deep transfer learning approach specifically tailored for direction-of-arrival(DOA)estimation.Our approach synergistically combines the residual attention network(RAN)with the bidirectional long short-term memory network(BiLSTM)to construct a robust pretrained model that efficiently captures and extracts abundant features from complex signal data.The model is initially pretrained on a large dataset,allowing it to learn rich feature representations that are later fine-tuned for deployment in scenarios with restricted snapshots.Through this careful fine-tuning process,the pretrained knowledge is effectively transferred,resulting in significantly optimized model parameters and improved estimation performance.Experimental findings clearly show that our proposed method not only considerably improves the DOA estimation accuracy but also maintains excellent robustness under challenging snapshot-restricted conditions,providing a promising new solution for real-time array prediction in environments with limited data availability.展开更多
To tackle the challenges of intractable parameter tun-ing,significant computational expenditure and imprecise model-driven sparse-based direction of arrival(DOA)estimation with array error(AE),this paper proposes a de...To tackle the challenges of intractable parameter tun-ing,significant computational expenditure and imprecise model-driven sparse-based direction of arrival(DOA)estimation with array error(AE),this paper proposes a deep unfolded amplitude-phase error self-calibration network.Firstly,a sparse-based DOA model with an array convex error restriction is established,which gets resolved via an alternating iterative minimization(AIM)algo-rithm.The algorithm is then unrolled to a deep network known as AE-AIM Network(AE-AIM-Net),where all parameters are opti-mized through multi-task learning using the constructed com-plete dataset.The results of the simulation and theoretical analy-sis suggest that the proposed unfolded network achieves lower computational costs compared to typical sparse recovery meth-ods.Furthermore,it maintains excellent estimation performance even in the presence of array magnitude-phase errors.展开更多
In recent years,the proliferation of beamforming signals has made the electromagnetic environment more complex.Traditional spectrum sensing techniques mainly focus on the detection of omnidirectional signals and can n...In recent years,the proliferation of beamforming signals has made the electromagnetic environment more complex.Traditional spectrum sensing techniques mainly focus on the detection of omnidirectional signals and can no longer meet the needs of beamforming signals.Moreover,the next-generation spectrum sensing technologies must not only reliably detect the presence of beamforming signals but also accurately estimate the spatial information of these signals.This paper investigates the issue of Direction of Arrival(DOA)of non-cooperative Unmanned Aerial Vehicle(UAV)beamforming signals,where most of the prior information about non-cooperative transmitters,such as the transmission power and the communication time slots,is unknown.In such conditions,we consider two types of data models for UAV beamforming signals with different Signalto-Noise Ratios(SNRs).Based on these data models,we develop a UAV Beamforming signals Detection-DOA Network(BD-DOANet),comprising convolutional modules,a channel attention module,and residual modules.Simulation results show that BD-DOANet effectively captures the angle information of non-cooperative UAV beamforming signals for both ideal and non-ideal data.At higher SNR levels,the average error is below 0.5 and its mean squared error is below 0.2.Even at lower SNR levels,BD-DOANet shows superior performance of DOA estimation.展开更多
In response to the issues of poor adaptability to low signal-to-noise ratios(SNRs)in existing uniform linear array(ULA)multitarget estimation algorithms and the difficulty of current deep learning methods in effective...In response to the issues of poor adaptability to low signal-to-noise ratios(SNRs)in existing uniform linear array(ULA)multitarget estimation algorithms and the difficulty of current deep learning methods in effectively extracting complex-valued features from data,a cross-scale sparse attention module and a channel-hierarchical spatial pyramid attention module,which are based on the MSPANet block,are introduced into the deep neural network(DNN).This approach better extracts multiscale features of signalling components,facilitating accurate signal feature extraction under low SNR conditions.Experimental data demonstrate that this deep learning model can significantly enhance the accuracy and anti-jamming capability of direction-of-arrival(DOA)estimation in low-signal-to-noise ratio(SNR)scenarios,outperforming traditional methods such as CBF,MUSIC,and ESPRIT.The above optimization achievements possess important practical value for DOA estimation applications in fields like intelligent speech,radar detection,communication systems,and autonomous driving.展开更多
文摘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。
文摘针对在现场可编程门阵列(Field Programmable Gate Array,FPGA)上实现基于极化敏感阵列的多重信号分类(Multiple Signal Classification,MUSIC)算法进行二维波达方向(Direction of Arrival,DOA)和二维极化参数联合估计时,硬件资源占用大、运行时间长的问题,提出了一种基于极化MUSIC算法的四维参数联合估计FPGA实现架构。该架构包括信号协方差矩阵计算模块、Jacobi旋转模块、噪声子空间提取模块、两级空间谱搜索模块和极化参数计算模块。Jacobi旋转模块被拆分为多个可复用模块,并采用查找表模块生成旋转矩阵。一级空间谱搜索模块通过二维DOA搜索初步确定信源的角度信息。二级空间谱搜索模块根据一级搜索的角度结果确定二级搜索区域各点的极化信息,并计算该区域的四维空间谱,区域内最小值对应的四维参数信息即为最终估计的信源方向角、俯仰角、极化辅助角和极化相位角。仿真结果表明,与传统极化MUSIC算法的四维搜索算法相比,该架构避免了大量四维空间谱计算,同时保证了四维参数估计的精度,显著减少了运行时间和硬件资源消耗。
文摘针对水下目标方位(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。
基金supported by the National Natural Science Foundation of China (62071144)
文摘Most of the existing direction of arrival(DOA)estimation algorithms are applied under the assumption that the array manifold is ideal.In practical engineering applications,the existence of non-ideal conditions such as mutual coupling between array elements,array amplitude and phase errors,and array element position errors leads to defects in the array manifold,which makes the performance of the algorithm decline rapidly or even fail.In order to solve the problem of DOA estimation in the presence of amplitude and phase errors and array element position errors,this paper introduces the first-order Taylor expansion equivalent model of the received signal under the uniform linear array from the Bayesian point of view.In the solution,the amplitude and phase error parameters and the array element position error parameters are regarded as random variables obeying the Gaussian distribution.At the same time,the expectation-maximization algorithm is used to update the probability distribution parameters,and then the two error parameters are solved alternately to obtain more accurate DOA estimation results.Finally,the effectiveness of the proposed algorithm is verified by simulation and experiment.
基金supported by China National Science Foundations(Nos.62371225,62371227)。
文摘Sparse array design has significant implications for improving the accuracy of direction of arrival(DOA)estimation of non-circular(NC)signals.We propose an extended nested array with a filled sensor(ENAFS)based on the hole-filling strategy.Specifically,we first introduce the improved nested array(INA)and prove its properties.Subsequently,we extend the sum-difference coarray(SDCA)by adding an additional sensor to fill the holes.Thus the larger uniform degrees of freedom(uDOFs)and virtual array aperture(VAA)can be abtained,and the ENAFS is designed.Finally,the simulation results are given to verify the superiority of the proposed ENAFS in terms of DOF,mutual coupling and estimation performance.
基金the financial support from the National Key Research and Development Program of China(No.2023YFB3907001)the National Natural Science Foundation of China(Nos.U2233217,62371029)the UK Engineering and Physical Sciences Research Council(EPSRC),China(Nos.EP/M026981/1,EP/T021063/1 and EP/T024917/)。
文摘Interference significantly impacts the performance of the Global Navigation Satellite Systems(GNSS),highlighting the need for advanced interference localization technology to bolster anti-interference and defense capabilities.The Uniform Circular Array(UCA)enables concurrent estimation of the Direction of Arrival(DOA)in both azimuth and elevation.Given the paramount importance of stability and real-time performance in interference localization,this work proposes an innovative approach to reduce the complexity and increase the robustness of the DOA estimation.The proposed method reduces computational complexity by selecting a reduced number of array elements to reconstruct a non-uniform sparse array from a UCA.To ensure DOA estimation accuracy,minimizing the Cramér-Rao Bound(CRB)is the objective,and the Spatial Correlation Coefficient(SCC)is incorporated as a constraint to mitigate side-lobe.The optimization model is a quadratic fractional model,which is solved by Semi-Definite Relaxation(SDR).When the array has perturbations,the mathematical expressions for CRB and SCC are re-derived to enhance the robustness of the reconstructed array.Simulation and hardware experiments validate the effectiveness of the proposed method in estimating interference DOA,showing high robustness and reductions in hardware and computational costs associated with DOA estimation.
文摘针对集中式框架下波达方向(direction of arrival, DOA)估计存在的计算量大、灵活性不足等问题,提出了基于扩展互质阵列的分布式DOA估计算法,实现以低计算量对目标DOA进行快速估计。首先,在每个子阵采用扩展互质阵布局,通过对局部采样协方差矩阵向量化构建差分虚拟阵列,识别并提取最长连续虚拟均匀线性阵列(uniform linear array, ULA)以去除空洞;随后,将DOA估计问题表述为基于角度网格的稀疏重构凸优化问题,并在分布式网络中构建基于共识的交替方向乘子法(alternating direction method of multipliers, ADMM)求解框架,使各子阵通过本地计算与融合中心协同恢复全局稀疏解。所提方法适用于大孔径、低成本及实时性要求高的大规模阵列信号处理场景。仿真实验从均方误差、运算时间等方面验证了所提算法的有效性。
基金funded by the Xinjiang Uygur Autonomous Region Natural Science Foundation General Program(Project Number:2023D01C18)the second batch of Tianchi Talents(Leading Talents)project in Xinjiang Uygur Autonomous Region.Project leader:Lei Liu from School of Computer Science and Technology,Xinjiang University.
文摘To solve the problem of performance degradation caused by insufficient array signal feature representation under limited snapshot conditions,this research proposes a novel deep transfer learning approach specifically tailored for direction-of-arrival(DOA)estimation.Our approach synergistically combines the residual attention network(RAN)with the bidirectional long short-term memory network(BiLSTM)to construct a robust pretrained model that efficiently captures and extracts abundant features from complex signal data.The model is initially pretrained on a large dataset,allowing it to learn rich feature representations that are later fine-tuned for deployment in scenarios with restricted snapshots.Through this careful fine-tuning process,the pretrained knowledge is effectively transferred,resulting in significantly optimized model parameters and improved estimation performance.Experimental findings clearly show that our proposed method not only considerably improves the DOA estimation accuracy but also maintains excellent robustness under challenging snapshot-restricted conditions,providing a promising new solution for real-time array prediction in environments with limited data availability.
基金supported by the National Natural Science Foundation of China(62301598).
文摘To tackle the challenges of intractable parameter tun-ing,significant computational expenditure and imprecise model-driven sparse-based direction of arrival(DOA)estimation with array error(AE),this paper proposes a deep unfolded amplitude-phase error self-calibration network.Firstly,a sparse-based DOA model with an array convex error restriction is established,which gets resolved via an alternating iterative minimization(AIM)algo-rithm.The algorithm is then unrolled to a deep network known as AE-AIM Network(AE-AIM-Net),where all parameters are opti-mized through multi-task learning using the constructed com-plete dataset.The results of the simulation and theoretical analy-sis suggest that the proposed unfolded network achieves lower computational costs compared to typical sparse recovery meth-ods.Furthermore,it maintains excellent estimation performance even in the presence of array magnitude-phase errors.
基金supported by the National Natural Science Foundation of China(Nos.U20B2038,62231027,62171462)。
文摘In recent years,the proliferation of beamforming signals has made the electromagnetic environment more complex.Traditional spectrum sensing techniques mainly focus on the detection of omnidirectional signals and can no longer meet the needs of beamforming signals.Moreover,the next-generation spectrum sensing technologies must not only reliably detect the presence of beamforming signals but also accurately estimate the spatial information of these signals.This paper investigates the issue of Direction of Arrival(DOA)of non-cooperative Unmanned Aerial Vehicle(UAV)beamforming signals,where most of the prior information about non-cooperative transmitters,such as the transmission power and the communication time slots,is unknown.In such conditions,we consider two types of data models for UAV beamforming signals with different Signalto-Noise Ratios(SNRs).Based on these data models,we develop a UAV Beamforming signals Detection-DOA Network(BD-DOANet),comprising convolutional modules,a channel attention module,and residual modules.Simulation results show that BD-DOANet effectively captures the angle information of non-cooperative UAV beamforming signals for both ideal and non-ideal data.At higher SNR levels,the average error is below 0.5 and its mean squared error is below 0.2.Even at lower SNR levels,BD-DOANet shows superior performance of DOA estimation.
基金funded by the Xinjiang Uygur Autonomous Region Natural Science Foundation General Program(Project Number:2023D01C18)the second batch of Tianchi Talents(Leading Talents)project in Xinjiang Uygur Autonomous Region.Project leader:Lei Liu from School of Computer Science and Technology,Xinjiang University.
文摘In response to the issues of poor adaptability to low signal-to-noise ratios(SNRs)in existing uniform linear array(ULA)multitarget estimation algorithms and the difficulty of current deep learning methods in effectively extracting complex-valued features from data,a cross-scale sparse attention module and a channel-hierarchical spatial pyramid attention module,which are based on the MSPANet block,are introduced into the deep neural network(DNN).This approach better extracts multiscale features of signalling components,facilitating accurate signal feature extraction under low SNR conditions.Experimental data demonstrate that this deep learning model can significantly enhance the accuracy and anti-jamming capability of direction-of-arrival(DOA)estimation in low-signal-to-noise ratio(SNR)scenarios,outperforming traditional methods such as CBF,MUSIC,and ESPRIT.The above optimization achievements possess important practical value for DOA estimation applications in fields like intelligent speech,radar detection,communication systems,and autonomous driving.
