k分布噪声中的极大似然和稳健G-music性能

Y. Abramovich, Ben A. Johnson, O. Besson
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引用次数: 1

摘要

对于k分布噪声下的m个点源信号的天线阵列输入混合,我们比较了最大似然法和最近引入的鲁棒G-MUSIC (RG-MUSIC)技术提供的DOA估计。我们证明了与高斯情况类似,MLE仍然优于RG-MUSIC,特别是在所谓的阈值区域内。这使得使用期望似然(EL)方法来检测RG-MUSIC性能崩溃的存在并通过先前为复杂高斯环境开发的方法“治愈”这些情况成为可能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Maximum likelihood and robust G-music performance in K-distributed noise
For an antenna array input mixture of m point source signals in K-distributed noise, we compare DOA estimation delivered by Maximum Likelihood and the recently introduced Robust G-MUSIC (RG-MUSIC) technique. We demonstrate that similar to the Gaussian case, MLE is still superior to RG-MUSIC, especially within the so-called threshold region. This makes it possible to use the Expected Likelihood (EL) methodology to detect the presence of RG-MUSIC performance breakdown and "cure" those cases via an approach previously developed for the complex Gaussian circumstance.
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