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一种基于PCA分析的DoA估计算法 被引量：4

Novel estimation algorithm for direction of arrival based on principal component analysis
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摘要 阵列信号处理中,MUSIC、ESPRIT等高分辨率DoA估计算法都要通过特征值分解来获得波达方向估计,然而矩阵特征值分解的计算量较大,不利于实时处理。提出使用PCA(principal component analysis)高效迭代算法,来逼近信号子空间矢量。该算法的计算过程相对简单,并具有自组织特性,适合于神经网络实现。仿真结果表明,所提算法的DoA估计性能与MUSIC算法相当。 Many high resolution algorithms based on subspace technology like MUSIC and ESPRIT estimate the direction of arrival of plane wave impinging on an array of sensors via eigendecomposition in array signal processing. However high computational burden for eigendecomposition makes them unsuitable for real time processing. An iterative algorithm for signal subspace estimation based on principal component analysis is proposed. The algorithm contains only relatively simple operations and has self organizing properties. Simulations show that the proposed algorithm has an analogy performance with MUSIC algorithm.
作者 陈洪光 沈振康 郭天天
机构地区 国防科技大学ATR国家重点实验室
出处 《系统工程与电子技术》 EI CSCD 北大核心 2005年第8期1376-1378,共3页 Systems Engineering and Electronics
关键词 波达方向 主成分分析 多重信号分类 direction of arrival principal component analysis multiple signal classification
分类号 TN911.22 [电子电信—通信与信息系统]
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参考文献9

  • 1IMdidi L, Radouane L. A neural network approach for DoA estimation and tracklng[A]. Proc. of the 10th IEEE Workshop on Statistical Signal and Array Processing [ C ]. Pocono Manor, PA.USA, 2000. 434 - 438.
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同被引文献17

  • 1黄磊,吴顺君,张林让,冯大政.快速子空间分解方法及其维数的快速估计[J].电子学报,2005,33(6):977-981. 被引量：44
  • 2贺宁蓉,吕善伟,常戎.子空间跟踪PASTd算法的改进及其应用[J].现代雷达,2005,27(11):75-77. 被引量：4
  • 3Schmidt R O. Multiple emitter location and signal parameter estimation[J].IEEE Trans. on Antennas Propagation, 1986,34(3): 276-286.
  • 4Xu G, Kailath T. Fast subspace decomposition[J].IEEE Trans Signal Processing, 1994,42(3):539-551.
  • 5Akaike H. A new look at the statistical model identification[J]. IEEE Trans. on AC, 1974, 19(6) : 716-723.
  • 6Haykin S. Neural Networks, h comprehensive Foundation[M]. Toranto, Ca:Macmillan College Publishing Company, 1994.
  • 7Sanger T D. Optimal Unsupervised Learning in a Single-Layer Linear FeedforwardNN[J].Neural Networks, 1989,2(4):459-473.
  • 8Badidi L, Radouane L. A neural network approach for DOA estimation and tracking[C]. In:Proc. of the 10^th IEEE Workshop on Statistical Signal and Array Processing, Pocono Manor, PA. USA, 2000, CSA:[s.n.]:434-438.
  • 9Schmidt RO. Multiple emitter location and signal parameter estimation[J]. IEEE Trans. on Antennas Propagation, 1986,34(3) :276 - 280.
  • 10Miao Yongfeng, Hua Yingbo. Fast subspace tracking and neural network learning by a novel information criterion[J].IEEE Trans. on Signal Processing, 1998,46 (7) : 1967- 1979.

引证文献4

二级引证文献2

系统工程与电子技术
2005年 第8期

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