Stochastic maximum-likelihood DOA estimation in the presence of unknown nonuniform noise
Stochastic maximum-likelihood DOA estimation in the presence of unknown nonuniform noise
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DOI：
10.1109/tsp.2008.917364
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发表时间：
2008-07-01
影响因子：
5.4
通讯作者：
Yao, Kung
中科院分区：
文献类型：
--
作者：
Chen, Chiao En;Lorenzelli, Flavio;Yao, Kung
本文研究了具有任意对角协方差矩阵的非均匀白噪声存在下多个窄带源的到达方向估计问题。尽管Pesavento和Gershman已经推导出该模型下的确定性和随机Cramer-Rao界（CRB）以及确定性最大似然（ML） DOA估计，但在相同设置下的随机ML DOA估计仍未在文献中得到。在此基础上，导出了一种新的随机ML DOA估计器。它的实现基于一个迭代过程，该过程以逐步的方式集中关于信号和噪声干扰参数的对数似然函数。提出了一种改进的逆迭代算法来估计噪声参数。仿真结果表明，该算法在非均匀噪声环境下比传统的均匀ML估计器性能有显著提高，并且只需要少量迭代即可收敛到非均匀随机CRB。
This correspondence investigates the direction-of-arrival (DOA) estimation of multiple narrowband sources in the presence of nonuniform white noise with an arbitrary diagonal covariance matrix. While both the deterministic and stochastic Cramer-Rao bound (CRB) and the deterministic maximum-likelihood (ML) DOA estimator under this model have been derived by Pesavento and Gershman, the stochastic ML DOA estimator under the same setting is still not available in the literature. In this correspondence, a new stochastic ML DOA estimator is derived. Its implementation is based on an iterative procedure which concentrates the log-likelihood function with respect to the signal and noise nuisance parameters in a stepwise fashion. A modified inverse iteration algorithm is also presented for the estimation of the noise parameters. Simulation results have shown that the proposed algorithm is able to provide significant performance improvement over the conventional uniform ML estimator in nonuniform noise environments and require only a few iterations to converge to the nonuniform stochastic CRB.