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.
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.
出处
《西安电子科技大学学报》
EI
CAS
CSCD
北大核心
2008年第6期1009-1014,共6页
Journal of Xidian University
基金
"十一五"预研基金资助(51307020401)
关键词
阵列
参数估计
信源个数估计
AIC
空间平滑
MDL
array
parameter estimation
estimation of number of sources
spatial smoothing AIC
Spatial smoothing MDL
