波达方向估计稀疏分解反问题准平稳信号Khatri-Rao积A Khatri-Rao (KR) sparse decomposition algorithm based on KR product with sparse signal representation is proposed in the light of direction of arrival (DOA) estimation of quasi-stationary signals. The received data are processed as the joint-sparse ...
To overcome this difficulty, we propose to apply the Khatri-Rao (KR) product array processing to the SAR Tomography which enhance the array aperture and... 山田,敏弘,山田,寛喜,山口,芳雄 - 《電子情報通信学会技術研究報告. a・p, アンテナ・伝播》 被引量: 0发表: 2012年 The Fgl2/fibr...
tionalMUSICalgorithmbyusingtheorthogonalitybetweenofymisN,theumpossibleDOFoftheKRproduct thenoisesubspaceandthesteeringvectorsofomingwaves.arrayisalsoN2,whiletheDOFoftheoriginalarrayisN. However,withCS-MUSICalgorithm,wecanestimateDOAsThisnumberisofcoursetoooptimistic,however,theDOFof evenwhenKLbytakingadvantageofco...
Khatri-Rao积联合稀疏分解 DOA估计方 法 刘庆 华 ,欧 阳缮 ,何振 清 (1.西安 电子 科技 大学 电子工 程 学院 ,陕西 西安 710071; 2.桂林 电子 科技 大 学信 息 与通信 学院 ,广西桂林 541004) 摘要 :针对 准平稳信 号的波达 方 向估计 ,提 出一种基 于 Khatri—Rao(KR)积的联 合稀 疏 分解 ...
巾图分类号0151.21 Abstract Inthis akindofblock dissertation,we matrixKroneckerwhichis study product claimedKhatri—Raoandestablishsome ofthe product block inequalities product。 Theresults consistofthreeinthis mainly parts paper. 1llthefirst Khatri-RanandKroneckeroftwomatricesare part,the products A.B re...
(DOA)estimationforquasi-stationarysignalsimping-ingonauniformcirculararray(UCA)withMsensorsisaddressedinthispaper.WeapplytheKhatri–Rao(KR)approachtotheUCAandobtainanewsignalmodelwhichiscapableofprovidingOðM2Þsensors.Meanwhile,thevirtualsteeringmatrixcanbedecom-posedintoaproductofcharacteristicmatrixdependingon...
Khatri-Rao ProductNathaniel E. Helwig
Khatri-Rao product of two matricesYiwen ZhangHua Zhou
Random Khatri-Rao-Product Codes for Numerically-Stable Distributed Matrix Multiplicationdoi:10.1109/ALLERTON.2019.8919859Adarsh M. SubramaniamAnoosheh HeidarzadehKrishna R. NarayananIEEE
Compared with the conventional CSM that simply averages the covariance matrices of different frequency bins after focusing, FKR transforms the covariance matrices into a higher dimensional matrix through KR product. This method has three major advantages: (1) it achieves a higher resolution than CSM,...