This paper considers multi-frequency passive radar and develops a multi-frequency joint direction of arrival(DOA)estimation algorithm to improve estimation accuracy and resolution.The developed algorithm exploits the ...This paper considers multi-frequency passive radar and develops a multi-frequency joint direction of arrival(DOA)estimation algorithm to improve estimation accuracy and resolution.The developed algorithm exploits the sparsity of targets in the spatial domain.Specifically,we first extract the required frequency channel data and acquire the snapshot data through a series of preprocessing such as clutter suppression,coherent integration,beamforming,and constant false alarm rate(CFAR)detection.Then,based on the framework of sparse Bayesian learning,the target’s DOA is estimated by jointly extracting the multi-frequency data via evidence maximization.Simulation results show that the developed algorithm has better estimation accuracy and resolution than other existing multi-frequency DOA estimation algorithms,especially under the scenarios of low signalto-noise ratio(SNR)and small snapshots.Furthermore,the effectiveness is verified by the field experimental data of a multi-frequency FM-based passive radar.展开更多
提出了一种采用酉ESPRIT(Unitary-Estimation ofSignal Parameters via Rotational Invariant Technique,Unitary-ESPRIT)算法对目标的二维波达方向(Direction-of-Arrival,DOA)进行估计,接收信号模型为中心对称的平面阵。与二维MUSIC(Mu...提出了一种采用酉ESPRIT(Unitary-Estimation ofSignal Parameters via Rotational Invariant Technique,Unitary-ESPRIT)算法对目标的二维波达方向(Direction-of-Arrival,DOA)进行估计,接收信号模型为中心对称的平面阵。与二维MUSIC(Multiple Signal Classification)算法、二维求根MUSIC算法、二维ESPRIT算法不同的是,该算法将复矩阵运算转化为实矩阵计算,简化了运算复杂程度,并且目标的DOA估计精度也相应的得到提高,是一种比较高效的DOA估计算法。展开更多
针对双平行线阵相干信号二维波达方向(DOA,Direction of Arrival)快速估计问题,利用信号的非圆特性,提出了一种基于非圆信号的单次快拍数据的二维DOA算法.建立新的阵列坐标系,对阵列接收单次快拍数据进行共轭运算,通过数据重新链接得到...针对双平行线阵相干信号二维波达方向(DOA,Direction of Arrival)快速估计问题,利用信号的非圆特性,提出了一种基于非圆信号的单次快拍数据的二维DOA算法.建立新的阵列坐标系,对阵列接收单次快拍数据进行共轭运算,通过数据重新链接得到伪快拍数据,阵列孔径扩展1倍.仅需要对3个伪单快拍矩阵的扩展矩阵进行一次奇异值分解即可实现二维DOA估计和解相干.该算法计算复杂度低,估计精度高,适用于对二维DOA估计实时性要求高的应用背景.计算机仿真结果验证了方法的有效性.展开更多
由于无网格(grid-less)稀疏重构方法的波达方向(direction of arrival,DOA)估计数学模型为单快拍形式,因此该方法只有在噪声电平趋近于零时才具有优越的性能.为了提高grid-less方法在信噪比(signal-to-noise ratio,SNR)较低时宽带相干...由于无网格(grid-less)稀疏重构方法的波达方向(direction of arrival,DOA)估计数学模型为单快拍形式,因此该方法只有在噪声电平趋近于零时才具有优越的性能.为了提高grid-less方法在信噪比(signal-to-noise ratio,SNR)较低时宽带相干信源的估计性能,提出了一种多快拍grid-less DOA估计方法.首先,对多快拍阵列观测矢量实施奇异值分解(singular value decomposition,SVD)获得观测矩阵的时域信号子空间,通过观测矩阵到时域信号子空间的投影实现观测矩阵的降噪;然后,为了不增加多快拍计算复杂度,将降噪后观测矩阵的列向量加权累加处理得到单快拍形式;最后,从理论上证明了本文提出的GL-SVD方法求解的模型是凸的,能够实现宽带信号DOA的精确重构.仿真结果表明,该方法在低SNR以及宽带相干信源情况下的估计精度都高于L 1范数最小化奇异值分解(L 1-norm minimum singular value decomposition,L 1-SVD)和离格稀疏贝叶斯推断奇异值分解(off-grid sparse Bayesian inference singular value decomposition,OGSBI-SVD),且在较小角度间隔的情况下具有更高的估计概率和分辨率.展开更多
基金supported by the National Natural Science Foundation of China(62071335,61931015,61831009)the Technological Innovation Project of Hubei Province of China(2019AAA061).
文摘This paper considers multi-frequency passive radar and develops a multi-frequency joint direction of arrival(DOA)estimation algorithm to improve estimation accuracy and resolution.The developed algorithm exploits the sparsity of targets in the spatial domain.Specifically,we first extract the required frequency channel data and acquire the snapshot data through a series of preprocessing such as clutter suppression,coherent integration,beamforming,and constant false alarm rate(CFAR)detection.Then,based on the framework of sparse Bayesian learning,the target’s DOA is estimated by jointly extracting the multi-frequency data via evidence maximization.Simulation results show that the developed algorithm has better estimation accuracy and resolution than other existing multi-frequency DOA estimation algorithms,especially under the scenarios of low signalto-noise ratio(SNR)and small snapshots.Furthermore,the effectiveness is verified by the field experimental data of a multi-frequency FM-based passive radar.
文摘提出了一种采用酉ESPRIT(Unitary-Estimation ofSignal Parameters via Rotational Invariant Technique,Unitary-ESPRIT)算法对目标的二维波达方向(Direction-of-Arrival,DOA)进行估计,接收信号模型为中心对称的平面阵。与二维MUSIC(Multiple Signal Classification)算法、二维求根MUSIC算法、二维ESPRIT算法不同的是,该算法将复矩阵运算转化为实矩阵计算,简化了运算复杂程度,并且目标的DOA估计精度也相应的得到提高,是一种比较高效的DOA估计算法。
文摘针对双平行线阵相干信号二维波达方向(DOA,Direction of Arrival)快速估计问题,利用信号的非圆特性,提出了一种基于非圆信号的单次快拍数据的二维DOA算法.建立新的阵列坐标系,对阵列接收单次快拍数据进行共轭运算,通过数据重新链接得到伪快拍数据,阵列孔径扩展1倍.仅需要对3个伪单快拍矩阵的扩展矩阵进行一次奇异值分解即可实现二维DOA估计和解相干.该算法计算复杂度低,估计精度高,适用于对二维DOA估计实时性要求高的应用背景.计算机仿真结果验证了方法的有效性.
文摘由于无网格(grid-less)稀疏重构方法的波达方向(direction of arrival,DOA)估计数学模型为单快拍形式,因此该方法只有在噪声电平趋近于零时才具有优越的性能.为了提高grid-less方法在信噪比(signal-to-noise ratio,SNR)较低时宽带相干信源的估计性能,提出了一种多快拍grid-less DOA估计方法.首先,对多快拍阵列观测矢量实施奇异值分解(singular value decomposition,SVD)获得观测矩阵的时域信号子空间,通过观测矩阵到时域信号子空间的投影实现观测矩阵的降噪;然后,为了不增加多快拍计算复杂度,将降噪后观测矩阵的列向量加权累加处理得到单快拍形式;最后,从理论上证明了本文提出的GL-SVD方法求解的模型是凸的,能够实现宽带信号DOA的精确重构.仿真结果表明,该方法在低SNR以及宽带相干信源情况下的估计精度都高于L 1范数最小化奇异值分解(L 1-norm minimum singular value decomposition,L 1-SVD)和离格稀疏贝叶斯推断奇异值分解(off-grid sparse Bayesian inference singular value decomposition,OGSBI-SVD),且在较小角度间隔的情况下具有更高的估计概率和分辨率.
