The problem of joint direction of arrival(DOA)and polarization estimation for polarization sensitive coprime planar arrays(PS-CPAs)is investigated,and a fast-convergence quadrilinear decomposition approach is proposed...The problem of joint direction of arrival(DOA)and polarization estimation for polarization sensitive coprime planar arrays(PS-CPAs)is investigated,and a fast-convergence quadrilinear decomposition approach is proposed.Specifically,we first decompose the PS-CPA into two sparse polarization sensitive uniform planar subarrays and employ propagator method(PM)to construct the initial steering matrices separately.Then we arrange the received signals into two quadrilinear models so that the potential DOA and polarization estimates can be attained via quadrilinear alternating least square(QALS).Subsequently,we distinguish the true DOA estimates from the approximate intersecting estimations of the two subarrays in view of the coprime feature.Finally,the polarization estimates paired with DOA can be obtained.In contrast to the conventional QALS algorithm,the proposed approach can remarkably reduce the computational complexity without degrading the estimation performance.Simulations demonstrate the superiority of the proposed fast-convergence approach for PS-CPAs.展开更多
The Direction of Arrival （DOA） estimation methods for underwater acoustic target using Temporally Multiple Sparse Bayesian Learning （TMSBL） as the reconstructing algorithm have the disadvantage of slow computing s...The Direction of Arrival （DOA） estimation methods for underwater acoustic target using Temporally Multiple Sparse Bayesian Learning （TMSBL） as the reconstructing algorithm have the disadvantage of slow computing speed. To solve this problem, a fast underwater acoustic target direction of arrival estimation was proposed. Analyzing the model characteristics of block-sparse Bayesian learning framework for DOA estimation, an algorithm was proposed to obtain the value of core hyper-parameter through MacKay＇s fixed-point method to estimate the DOA. By this process, it will spend less time for computation and provide more superior recovery performance than TMSBL algorithm. Simulation results verified the feasibility and effectiveness of the proposed algorithm.展开更多
提出一种基于信号子空间转换法(signal subspacetransform,SST)与快速子空间测向算法(fast subspaceestimation of DOA,FDOA)的局放超声阵列信号高精度测向新方法。首先利用SST算法对局放超声阵列信号进行聚焦处理,使得原始信号的宽频...提出一种基于信号子空间转换法(signal subspacetransform,SST)与快速子空间测向算法(fast subspaceestimation of DOA,FDOA)的局放超声阵列信号高精度测向新方法。首先利用SST算法对局放超声阵列信号进行聚焦处理,使得原始信号的宽频空域信息被最大限度地保留,从而实现宽带信号方位信息的累积。然后采用FDOA对聚焦后的窄带信号进行波达方向估计,FDOA无需特征分解,无需估计整个协方差矩阵,可提高运算速度,且具有更高的测向精度。在此基础之上,应用医学宽频超声信号和4 4超声阵列传感器模型,进行局放超声阵列信号测向仿真研究,仿真结果表明,测向误差小于2,验证了该方法的正确性。展开更多
将声矢量传感器阵列参数估计问题与平行因子(Parallel factor,PARAFAC)模型相结合,提出了一种基于快速PARAFAC分解的二维波达方向(Direction of arrival,DOA)估计算法。该算法首先将接收信号构建为PARAFAC模型,然后在数据域对参数矩阵...将声矢量传感器阵列参数估计问题与平行因子(Parallel factor,PARAFAC)模型相结合,提出了一种基于快速PARAFAC分解的二维波达方向(Direction of arrival,DOA)估计算法。该算法首先将接收信号构建为PARAFAC模型,然后在数据域对参数矩阵进行初估计,最后利用PARAFAC分解获得信号二维DOA估计。该算法能够应用于任意结构的声矢量传感器阵列,同时能够得到和信源一一匹配的仰角和方位角估计。借助于参数矩阵的初始估计,所提算法收敛速度较快,其计算复杂度大大降低。该算法角度估计性能接近于PARAFAC算法,同时优于借助旋转不变性进行信号参数估计(Estimation of signal parameters via rotational invariance technique,ESPRIT)算法和传播算子(Propagator method,PM)算法。展开更多
基金supported by the Open Research Fund of the State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System(No.CEMEE2019Z0104B)。
文摘The problem of joint direction of arrival(DOA)and polarization estimation for polarization sensitive coprime planar arrays(PS-CPAs)is investigated,and a fast-convergence quadrilinear decomposition approach is proposed.Specifically,we first decompose the PS-CPA into two sparse polarization sensitive uniform planar subarrays and employ propagator method(PM)to construct the initial steering matrices separately.Then we arrange the received signals into two quadrilinear models so that the potential DOA and polarization estimates can be attained via quadrilinear alternating least square(QALS).Subsequently,we distinguish the true DOA estimates from the approximate intersecting estimations of the two subarrays in view of the coprime feature.Finally,the polarization estimates paired with DOA can be obtained.In contrast to the conventional QALS algorithm,the proposed approach can remarkably reduce the computational complexity without degrading the estimation performance.Simulations demonstrate the superiority of the proposed fast-convergence approach for PS-CPAs.
基金supported by the National Natural Science Foundation of China(11574120,U1636117)the Open Project Program of the Key Laboratory of Underwater Acoustic Signal Processing,Ministry of Education,China(UASP1503)+1 种基金the Natural Science Foundation of Jiangsu Province of China(BK20161359)Foundation of Key Laboratory of Underwater Acoustic Warfare Technology of China and Qing Lan Project
文摘The Direction of Arrival （DOA） estimation methods for underwater acoustic target using Temporally Multiple Sparse Bayesian Learning （TMSBL） as the reconstructing algorithm have the disadvantage of slow computing speed. To solve this problem, a fast underwater acoustic target direction of arrival estimation was proposed. Analyzing the model characteristics of block-sparse Bayesian learning framework for DOA estimation, an algorithm was proposed to obtain the value of core hyper-parameter through MacKay＇s fixed-point method to estimate the DOA. By this process, it will spend less time for computation and provide more superior recovery performance than TMSBL algorithm. Simulation results verified the feasibility and effectiveness of the proposed algorithm.
文摘将声矢量传感器阵列参数估计问题与平行因子(Parallel factor,PARAFAC)模型相结合,提出了一种基于快速PARAFAC分解的二维波达方向(Direction of arrival,DOA)估计算法。该算法首先将接收信号构建为PARAFAC模型,然后在数据域对参数矩阵进行初估计,最后利用PARAFAC分解获得信号二维DOA估计。该算法能够应用于任意结构的声矢量传感器阵列,同时能够得到和信源一一匹配的仰角和方位角估计。借助于参数矩阵的初始估计,所提算法收敛速度较快,其计算复杂度大大降低。该算法角度估计性能接近于PARAFAC算法,同时优于借助旋转不变性进行信号参数估计(Estimation of signal parameters via rotational invariance technique,ESPRIT)算法和传播算子(Propagator method,PM)算法。
