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首页> 外文期刊>Aerospace and Electronic Systems, IEEE Transactions on >A Kullback-Leibler Methodology for Unconditional ML DOA Estimation in Unknown Nonuniform Noise
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A Kullback-Leibler Methodology for Unconditional ML DOA Estimation in Unknown Nonuniform Noise

机译：未知非均匀噪声中无条件ML DOA估计的Kullback-Leibler方法

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摘要

Maximum likelihood (ML) direction-of arrival (DOA) estimation of multiple narrowband sources in unknown nonunifrom white noise is considered. A new iterative algorithm for stochastic ML DOA estimation is presented. The stepwise concentration of the log-likelihood (LL) function with respect to the signal and noise nuisance parameters is derived by alternating minimization of the Kullback-Leibler divergence between a model family of probability distributions defined on the unconditional model and a desired family of probability distributions constrained to be concentrated on the observed data. The new algorithm presents the advantage to provide closed-form expressions for the signal and noise nuisance parameter estimates which results in a substantial reduction of the parameter space required for numerical optimization. The proposed algorithm converges only after a few iterations and its effectiveness is confirmed in a simulation example.
机译：考虑了未知非均匀白噪声中多个窄带源的最大似然（ML）到达方向（DOA）估计。提出了一种新的随机ML DOA估计迭代算法。对数似然（LL）函数相对于信号和噪声扰动参数的逐步集中是通过在无条件模型上定义的概率分布的模型族与期望的概率族之间的Kullback-Leibler散度的交替最小化而得出的分布被约束为集中在观察到的数据上。新算法具有为信号和噪声扰动参数估计提供封闭形式的表达式的优势，从而大大减少了数值优化所需的参数空间。所提出的算法仅在几次迭代后收敛，并且在仿真示例中证实了其有效性。

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