NEAR-FIELD MULTIPLE SOURCE LOCALIZATION BY PASSIVE SENSOR ARRAY
NEAR-FIELD MULTIPLE SOURCE LOCALIZATION BY PASSIVE SENSOR ARRAY
复制标题
DOI：
10.1109/8.86917
复制
发表时间：
1991-07-01
影响因子：
5.7
通讯作者：
BARKAT, M
中科院分区：
文献类型：
--
作者：
HUANG, YD;BARKAT, M
研究了空间高斯白噪声环境中多个近场源的定位问题。首先，我们使用改进的二维(2-D)多信号分类(MUSIC)算法来定位信号源：距离和方向。然后，我们考虑了一种全局最优的最大似然搜索方法来定位这些信源。结果表明，在单源情况下，随着快照数的增加，二维MUSIC、估计器和最大似然估计器(MLE)的协方差均逼近Cramer-Rao下界(CRLB)。在多源情况下，我们观察到在高信噪比和大量快照的情况下，两种定位技术的均方根误差(RMSE)都相对较小。然而，在低信噪比和/或少量快照的情况下，最大似然估计的性能明显优于改进的二维MUSIC。
A study is presented of the localization of multiple near-field sources in a spatially white Gaussian noise environment. First, we use a modified two-dimensional (2-D) version of the multiple signal classification (MUSIC) algorithm to localize the signal sources; range and bearing. Then, we consider a global-optimum maximum likelihood searching approach to localize these sources. It is shown that in the single source situation, the covariances of both the 2-D MUSIC, estimator and the maximum likelihood estimator (MLE) approach the Cramer-Rao lower bound (CRLB) as the number of snapshots increases to infinity. In the multiple source situation, we observe that for a high signal-to-noise ratio (SNR) and a large number of snapshots, the root mean square errors (RMSE's) of both localization techniques are relatively small. However, for low SNR and/or small number of snapshots, the performance of the MLE is much superior than that of the modified 2-D MUSIC.