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  1. (1. 西安电子科技大学 理学院,陕西 西安 710071;
    2. 西安电子科技大学 雷达信号处理重点实验室,陕西 西安 710071)
  • 收稿日期:2008-06-02 修回日期:1900-01-01 出版日期:2008-12-20 发布日期:2008-12-20
  • 通讯作者: 安志娟

Estimation of the number of sources based on spatial smoothing

AN Zhi-juan1,2;SU Hong-tao2;BAO Zheng2

  1. (1. School of Science, Xidian Univ., Xi’an 710071, China;
    2. Key Lab. of Radar Signal Processing, Xidian Univ., Xi’an 710071, China)
  • Received:2008-06-02 Revised:1900-01-01 Online:2008-12-20 Published:2008-12-20
  • Contact: AN Zhi-juan

摘要: 针对小快拍数和阵元噪声功率不等时噪声特征值分散严重,导致Akaike信息论准则(AIC)和最小描述长度准则(MDL)估计信源个数效果差的问题,提出了基于空间平滑的AIC方法(SSAIC)和MDL方法(SSMDL),该方法利用前后向平滑有效降低噪声特征值的分散程度,从而提高正确估计概率,并证明了SSMDL方法的一致性.仿真结果表明该方法在小快拍和阵元噪声功率不等情况下可以显著提高正确检验概率.

关键词: 阵列, 参数估计, 信源个数估计, 空间平滑的AIC, 空间平滑的MDL

Abstract: In the context of a small number of snapshots or unequal noise levels the noise eigenvalues of the covariance matrix are spreading, which results in the performance deterioration of the Akaike information criterion(AIC) and the Minimum Description Length(MDL). In this paper the Spatial Smoothing AIC(SSAIC) and Spatial Smoothing MDL(SSMDL) are presented. By spatial smoothing the spreading of the noise eigenvalues can be reduced remarkably, and hence the probability of correct detection can be increased, in addition, the consistency of the SSMDL is proved in detail. Finally, simulation results show that the SSAIC and SSMDL can improve the probability of correct detection remarkably.

Key words: array, parameter estimation, estimation of number of sources, spatial smoothing AIC, Spatial smoothing MDL


  • TN911.7