ROBUST MAXIMUM-LIKELIHOOD BEARING ESTIMATION IN CONTAMINATED GAUSSIAN-NOISE
ROBUST MAXIMUM-LIKELIHOOD BEARING ESTIMATION IN CONTAMINATED GAUSSIAN-NOISE
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DOI：
10.1109/78.149999
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发表时间：
1992-08-01
影响因子：
5.4
通讯作者：
KASHYAP, RL
中科院分区：
文献类型：
--
作者：
LEE, DD;KASHYAP, RL
提出了一种对高斯噪声中异常值和分布不确定性不敏感的鲁棒最大似然到达方向估计方法。当底层噪声分布稍微偏离高斯分布时，该算法表现得比高斯ML算法好得多，而在纯高斯噪声中仍然表现得几乎一样好。与高斯机器学习估计一样，它仍然能够处理相关信号以及单个快照情况。使用我们独特的分辨率测试程序来分析算法的性能，该测试程序确定在给定的置信度水平下，DOA估计算法是否可以解析两个DOA非常接近的主要源。
A robust maximum likelihood (ML) direction-of-arrival (DOA) estimation method which is insensitive to outliers and distributional uncertainties in Gaussian noise is presented. The algorithm has been shown to perform much better than the Gaussian ML algorithm when the underlying noise distribution deviates even slightly from Gaussian while still performing almost as well in pure Gaussian noise. As with the Gaussian ML estimation, it is still capable of handling correlated signals as well as single snapshot cases. Performance of the algorithm is analyzed using our unique resolution test procedure which determines whether a DOA estimation algorithm, at a given confidence level, can resolve two dominant sources with very close DOA's or not.