The Khatri-Rao(KR) subspace method is a high resolution method for direction-of-arrival(DOA) estimation.Combined with 2q level nested array,the KR subspace method can detect O(N2q) sources with N sensors.However,the m...The Khatri-Rao(KR) subspace method is a high resolution method for direction-of-arrival(DOA) estimation.Combined with 2q level nested array,the KR subspace method can detect O(N2q) sources with N sensors.However,the method cannot be applicable to Gaussian sources when q is equal to or greater than 2 since it needs to use 2q-th order cumulants.In this work,a novel approach is presented to conduct DOA estimation by constructing a fourth order difference co-array.Unlike the existing DOA estimation method based on the KR product and 2q level nested array,the proposed method only uses second order statistics,so it can be employed to Gaussian sources as well as non-Gaussian sources.By exploiting a four-level nested array with N elements,our method can also identify O(N4) sources.In order to estimate the wideband signals,the proposed method is extended to the wideband scenarios.Simulation results demonstrate that,compared to the state of the art KR subspace based methods,the new method achieves higher resolution.展开更多
对分块实对称正定矩阵A，B，C和D，证明了一个矩阵等式( A ⊙ B ) # ( C ⊙ D ) = ( A # C ) ⊙ ( B # D )，这里A ⊙ B和A # B分别是A与B的Tracy-Singh乘积和几何平均，如果A和B是分块实对称矩阵，则有矩阵不等式 ≥ ，其中是矩阵和的K...对分块实对称正定矩阵A，B，C和D，证明了一个矩阵等式( A ⊙ B ) # ( C ⊙ D ) = ( A # C ) ⊙ ( B # D )，这里A ⊙ B和A # B分别是A与B的Tracy-Singh乘积和几何平均，如果A和B是分块实对称矩阵，则有矩阵不等式 ≥ ，其中是矩阵和的Khatri -Rao乘积。展开更多
为了进一步提高分布式阵列的自由度和分辨力,提出一种分布式nested阵列。该阵列将nested阵列作为分布式阵列的子阵。基于Khatri-Rao积,nested子阵可提高整个阵列的自由度。分布式nested阵列以较少的阵元数及硬件成本实现大的孔径和较高...为了进一步提高分布式阵列的自由度和分辨力,提出一种分布式nested阵列。该阵列将nested阵列作为分布式阵列的子阵。基于Khatri-Rao积,nested子阵可提高整个阵列的自由度。分布式nested阵列以较少的阵元数及硬件成本实现大的孔径和较高的分辨力,而且提高了目标波达方向(direction of arrival,DOA)估计的精度。并利用基于Khatri-Rao积的空间平滑酉旋转不变子空间(estimation of signal parameters via rotational invariance techniques,ESPRIT)算法进行DOA估计。其先对协方差矩阵向量化提高自由度,然后利用空间平滑对新数据协方差矩阵进行秩恢复,最后使用双尺度酉ESPRIT算法得到DOA估计。仿真结果证明所提方法的有效性。展开更多
基金Project(2010ZX03006-004) supported by the National Science and Technology Major Program of ChinaProject(YYYJ-1113) supported by the Knowledge Innovation Program of the Chinese Academy of SciencesProject(2011CB302901) supported by the National Basic Research Program of China
文摘The Khatri-Rao(KR) subspace method is a high resolution method for direction-of-arrival(DOA) estimation.Combined with 2q level nested array,the KR subspace method can detect O(N2q) sources with N sensors.However,the method cannot be applicable to Gaussian sources when q is equal to or greater than 2 since it needs to use 2q-th order cumulants.In this work,a novel approach is presented to conduct DOA estimation by constructing a fourth order difference co-array.Unlike the existing DOA estimation method based on the KR product and 2q level nested array,the proposed method only uses second order statistics,so it can be employed to Gaussian sources as well as non-Gaussian sources.By exploiting a four-level nested array with N elements,our method can also identify O(N4) sources.In order to estimate the wideband signals,the proposed method is extended to the wideband scenarios.Simulation results demonstrate that,compared to the state of the art KR subspace based methods,the new method achieves higher resolution.
文摘对分块实对称正定矩阵A，B，C和D，证明了一个矩阵等式( A ⊙ B ) # ( C ⊙ D ) = ( A # C ) ⊙ ( B # D )，这里A ⊙ B和A # B分别是A与B的Tracy-Singh乘积和几何平均，如果A和B是分块实对称矩阵，则有矩阵不等式 ≥ ，其中是矩阵和的Khatri -Rao乘积。
文摘为了进一步提高分布式阵列的自由度和分辨力,提出一种分布式nested阵列。该阵列将nested阵列作为分布式阵列的子阵。基于Khatri-Rao积,nested子阵可提高整个阵列的自由度。分布式nested阵列以较少的阵元数及硬件成本实现大的孔径和较高的分辨力,而且提高了目标波达方向(direction of arrival,DOA)估计的精度。并利用基于Khatri-Rao积的空间平滑酉旋转不变子空间(estimation of signal parameters via rotational invariance techniques,ESPRIT)算法进行DOA估计。其先对协方差矩阵向量化提高自由度,然后利用空间平滑对新数据协方差矩阵进行秩恢复,最后使用双尺度酉ESPRIT算法得到DOA估计。仿真结果证明所提方法的有效性。
