展开互质阵列将两个子阵完全展开,因而可在阵元数目受限情况下获得相较于均匀阵列以及传统互质阵列更大的阵列孔径。文中基于双基地展开互质阵列多输入多输出(Multiple Input Multiple Output,MIMO)雷达阵列结构,提出了基于降维多重信...展开互质阵列将两个子阵完全展开,因而可在阵元数目受限情况下获得相较于均匀阵列以及传统互质阵列更大的阵列孔径。文中基于双基地展开互质阵列多输入多输出(Multiple Input Multiple Output,MIMO)雷达阵列结构,提出了基于降维多重信号分类(Multiple Signal Classification,MUSIC)算法的双基地展开互质阵列MIMO雷达离开角(Direction of Departure,DOD)、到达角(Direction of Arrival,DOA)联合估计算法。算法通过增加约束并构造代价函数的方式,将二维MUSIC算法中的穷尽搜索二维谱峰转化为求解带约束二次优化问题,先后得到DOA、DOD,并且DOD与DOA自动配对。降维思想的引入使得算法无需二维搜索,因而复杂度显著下降。同时,得益于展开互质阵列MIMO雷达形成的虚拟阵列与大幅扩展的阵列孔径,文中提出的算法亦获得了显著提升的分辨率、自由度以及低信噪比下更为优异的估计性能。此外,子阵数目的互质消除了阵元间距大于半波长可能导致的相位模糊问题。仿真验证了算法的有效性。展开更多
实值处理具有降低高自由度多输入多输出(multiple-input multiple-output,MIMO)雷达角度估计大计算量的优势。但受制于阵列的共轭对称性,对于任意阵列结构的双基地MIMO雷达发射角(direction of departure,DOD)和接收角(direction of arr...实值处理具有降低高自由度多输入多输出(multiple-input multiple-output,MIMO)雷达角度估计大计算量的优势。但受制于阵列的共轭对称性,对于任意阵列结构的双基地MIMO雷达发射角(direction of departure,DOD)和接收角(direction of arrival,DOA)联合估计,若不做附加的预处理则无法实现实值操作,故将常规阵列实值处理的多重信号分类(multiple signal classification,MUSIC)超分辨算法推广至任意阵列结构的双基地MIMO雷达。首先根据MIMO雷达的导向矢量共轭与镜像的对等性,提取接收信号协方差矩阵的实部,并对其进行特征分解得到"目标加倍"的信号子空间及其应对的噪声子空间;然后利用Kronecker积的特性对其进行降维处理,得到搜索区域减半的一维半实值域MUSIC谱,取出目标DOD真值与其镜像代入降维Capon算法来剔除虚拟峰值得到目标DOD估计真值;最后利用特征矢量得到模糊DOA估计值,采用方向余弦差最小范数方法得到目标DOA无模糊估计值。本文算法估计性能与一维搜索复数域MUSIC相当,计算量约降50%,且能够实现DOD和DOA的自动配对。仿真结果证明了该算法的有效性。展开更多
多输入多输出(Multiple-input multiple-output,MIMO)雷达利用多个天线发送和接收信号,具有超过传统相控阵的潜在优势。本文提出一种双基地MIMO雷达中基于传播算子的离开角(Direction of departure,DOD)和到达角(Direction of arrival,D...多输入多输出(Multiple-input multiple-output,MIMO)雷达利用多个天线发送和接收信号,具有超过传统相控阵的潜在优势。本文提出一种双基地MIMO雷达中基于传播算子的离开角(Direction of departure,DOD)和到达角(Direction of arrival,DOA)估计算法。该算法利用传播因子避免了对协方差矩阵特征值分解降低了运算的复杂度,并且在低信噪比和低快拍数的情况下,该算法仍具有良好的性能。与快速多目标定位算法相比,本文算法的角度估计性能有很大的提高。文中还推导出了离开角和到达角估计的均方误差。仿真结果证明了该算法的有效性。展开更多
Measuring the business-IT alignment(BITA)of an organization determines its alignment level,provides directions for further improvements,and consequently promotes the organizational performances.Due to the capabilities...Measuring the business-IT alignment(BITA)of an organization determines its alignment level,provides directions for further improvements,and consequently promotes the organizational performances.Due to the capabilities of enterprise architecture(EA)in interrelating different business/IT viewpoints and elements,the development of EA is superior to support BITA measurement.Extant BITA measurement literature is sparse when it concerns EA.The literature tends to explain how EA viewpoints or models correlate with BITA,without discussing where to collect and integrate EA data.To address this gap,this paper attempts to propose a specific BITA measurement process through associating a BITA maturity model with a famous EA framework:DoD Architectural Framework 2.0(DoDAF2.0).The BITA metrics in the maturity model are connected to the meta-models and models of DoDAF2.0.An illustrative ArchiSurance case is conducted to explain the measurement process.Systematically,this paper explores the process of BITA measurement from the viewpoint of EA,which helps to collect the measurement data in an organized way and analyzes the BITA level in the phase of architecture development.展开更多
结合分布式阵列和双基地多输入多输出(Multiple-Input Multiple-Output,MIMO)雷达的特点,提出了一种新的双基地分布式阵列MIMO雷达的接收角(Direction of Arrival,DOA)和发射角(Direction of Departure,DOD)估计方法.根据发射阵列和接...结合分布式阵列和双基地多输入多输出(Multiple-Input Multiple-Output,MIMO)雷达的特点,提出了一种新的双基地分布式阵列MIMO雷达的接收角(Direction of Arrival,DOA)和发射角(Direction of Departure,DOD)估计方法.根据发射阵列和接收阵列的空域旋转不变特性,利用旋转不变估计技术(Estimation of Signal Parameters via Rotational Invariance Techniques,ESPRIT)获取无模糊DOA粗估计和高精度周期性模糊的DOA、DOD精估计;再利用无模糊DOA粗估计、目标的双基地距离信息以及双基地MIMO雷达的几何特点,解除DOA、DOD精估计的周期性模糊,得到高精度且无模糊的DOA和DOD估计.最后,根据ESPRIT算法原理和估计误差的概率统计特性进行算法的性能分析,给出算法基线模糊门限的近似计算方法.该算法有效地放宽了发射阵列孔径扩展程度的限制,从而提高了阵列在大孔径下的角度估计精度,且能够实现DOA和DOD估计的自动配对.仿真结果验证了所提算法和性能分析方法的有效性.展开更多
文摘展开互质阵列将两个子阵完全展开,因而可在阵元数目受限情况下获得相较于均匀阵列以及传统互质阵列更大的阵列孔径。文中基于双基地展开互质阵列多输入多输出(Multiple Input Multiple Output,MIMO)雷达阵列结构,提出了基于降维多重信号分类(Multiple Signal Classification,MUSIC)算法的双基地展开互质阵列MIMO雷达离开角(Direction of Departure,DOD)、到达角(Direction of Arrival,DOA)联合估计算法。算法通过增加约束并构造代价函数的方式,将二维MUSIC算法中的穷尽搜索二维谱峰转化为求解带约束二次优化问题,先后得到DOA、DOD,并且DOD与DOA自动配对。降维思想的引入使得算法无需二维搜索,因而复杂度显著下降。同时,得益于展开互质阵列MIMO雷达形成的虚拟阵列与大幅扩展的阵列孔径,文中提出的算法亦获得了显著提升的分辨率、自由度以及低信噪比下更为优异的估计性能。此外,子阵数目的互质消除了阵元间距大于半波长可能导致的相位模糊问题。仿真验证了算法的有效性。
文摘实值处理具有降低高自由度多输入多输出(multiple-input multiple-output,MIMO)雷达角度估计大计算量的优势。但受制于阵列的共轭对称性,对于任意阵列结构的双基地MIMO雷达发射角(direction of departure,DOD)和接收角(direction of arrival,DOA)联合估计,若不做附加的预处理则无法实现实值操作,故将常规阵列实值处理的多重信号分类(multiple signal classification,MUSIC)超分辨算法推广至任意阵列结构的双基地MIMO雷达。首先根据MIMO雷达的导向矢量共轭与镜像的对等性,提取接收信号协方差矩阵的实部,并对其进行特征分解得到"目标加倍"的信号子空间及其应对的噪声子空间;然后利用Kronecker积的特性对其进行降维处理,得到搜索区域减半的一维半实值域MUSIC谱,取出目标DOD真值与其镜像代入降维Capon算法来剔除虚拟峰值得到目标DOD估计真值;最后利用特征矢量得到模糊DOA估计值,采用方向余弦差最小范数方法得到目标DOA无模糊估计值。本文算法估计性能与一维搜索复数域MUSIC相当,计算量约降50%,且能够实现DOD和DOA的自动配对。仿真结果证明了该算法的有效性。
文摘多输入多输出(Multiple-input multiple-output,MIMO)雷达利用多个天线发送和接收信号,具有超过传统相控阵的潜在优势。本文提出一种双基地MIMO雷达中基于传播算子的离开角(Direction of departure,DOD)和到达角(Direction of arrival,DOA)估计算法。该算法利用传播因子避免了对协方差矩阵特征值分解降低了运算的复杂度,并且在低信噪比和低快拍数的情况下,该算法仍具有良好的性能。与快速多目标定位算法相比,本文算法的角度估计性能有很大的提高。文中还推导出了离开角和到达角估计的均方误差。仿真结果证明了该算法的有效性。
基金supported by the National Natural Science Foundation of China(71571189)the State Key Laboratory of Air Traffic Management System and Technology(SKLATM201806)
文摘Measuring the business-IT alignment(BITA)of an organization determines its alignment level,provides directions for further improvements,and consequently promotes the organizational performances.Due to the capabilities of enterprise architecture(EA)in interrelating different business/IT viewpoints and elements,the development of EA is superior to support BITA measurement.Extant BITA measurement literature is sparse when it concerns EA.The literature tends to explain how EA viewpoints or models correlate with BITA,without discussing where to collect and integrate EA data.To address this gap,this paper attempts to propose a specific BITA measurement process through associating a BITA maturity model with a famous EA framework:DoD Architectural Framework 2.0(DoDAF2.0).The BITA metrics in the maturity model are connected to the meta-models and models of DoDAF2.0.An illustrative ArchiSurance case is conducted to explain the measurement process.Systematically,this paper explores the process of BITA measurement from the viewpoint of EA,which helps to collect the measurement data in an organized way and analyzes the BITA level in the phase of architecture development.
文摘结合分布式阵列和双基地多输入多输出(Multiple-Input Multiple-Output,MIMO)雷达的特点,提出了一种新的双基地分布式阵列MIMO雷达的接收角(Direction of Arrival,DOA)和发射角(Direction of Departure,DOD)估计方法.根据发射阵列和接收阵列的空域旋转不变特性,利用旋转不变估计技术(Estimation of Signal Parameters via Rotational Invariance Techniques,ESPRIT)获取无模糊DOA粗估计和高精度周期性模糊的DOA、DOD精估计;再利用无模糊DOA粗估计、目标的双基地距离信息以及双基地MIMO雷达的几何特点,解除DOA、DOD精估计的周期性模糊,得到高精度且无模糊的DOA和DOD估计.最后,根据ESPRIT算法原理和估计误差的概率统计特性进行算法的性能分析,给出算法基线模糊门限的近似计算方法.该算法有效地放宽了发射阵列孔径扩展程度的限制,从而提高了阵列在大孔径下的角度估计精度,且能够实现DOA和DOD估计的自动配对.仿真结果验证了所提算法和性能分析方法的有效性.
