%0 Journal Article
%T 智能天线DOA估计技术研究
%A 王
%A 莉
%A 夏克文
%A 姜
%A 霞
%A 孟
%A 瑶
%J 河北工业大学学报
%D 2018
%R 10.14081/j.cnki.hgdxb.2018.03.001
%X 智能天线DOA估计技术中子空间分解类算法存在计算量大和采样数据多的缺点，为实现实时准确的 DOA估计，提出一种在局部信号空间搜索谱峰的改进MUSIC算法，与经典算法仿真对比，结果表明改进算法 运算量明显降低．此外，为克服传统算法采样数据量大且存在冗余的不足，研究基于压缩感知的DOA估计方 法，即由阵列数据通过阵列流型矩阵重构出空间稀疏信号，从而估计目标信号的DOA，实验结果表明该方法 估计效果显著，且性能优于传统算法．</br>AsakindofDOAestimationtechnologyofsmartantenna,thesubspacedecompositionalgorithmhassuch disadvantagesasthelargecomputationalamountandmoresamplingdata.Inordertoachievereal-timeandaccurate DOAestimation,theimprovedMUSICalgorithmthatsearchingspectralpeakinlocalspectrumspaceisputforward. Comparedwiththeclassicalalgorithm,thesimulationresultsshowthatthecomputationalamountoftheimprovedalgo? rithmisreducedsignificantly.Inaddition,toovercometheshortcomingsoflargeamountofsamplingdataandredundan? cyintraditionalalgorithms,weadopttheDOAestimationbasedoncompressedsensingi.e.thespacesparsesignalisre? constructedfromthearraydatabymeansofarraymanifoldmatrix,thentheDOAofthetargetsignalcanbeestimated. Thesimulationresultsshowthatthemethodhasasignificantestimationeffect,andtheperformanceisbetterthanthetra? ditionalalgorithm.
%K DOA估计
%K MUSIC算法
%K ESPRIT算法
%K 压缩感知&lt
%K /br&gt
%K DOAEstimation
%K MUSICalgorithm
%K ESPRITalgorithm
%K compressivesensing
%U http://zrxuebao.hebut.edu.cn//oa/darticle.aspx?type=view&id=201803001