Joint DOA and Polarization Estimation for Unequal Power Sources
Joint DOA and Polarization Estimation for Unequal Power Sources
复制标题
不等功率源的联合DOA和偏振估计
DOI：
10.1155/2015/471374
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发表时间：
2015-02
影响因子：
1.5
通讯作者：
Wenyi Dong
中科院分区：
文献类型：
--
作者：
Qingyuan Fang;Yong Han;Ming Jin;Wenyi Dong
对于大多数的DOA和极化联合估计方法，没有讨论所提方法在处理不等功率源时的性能。然而，在实际应用中，权力不等的源显然广泛存在。本文利用噪声子空间（IPNS）对功率的不变性，提出了一种不等功率源的联合DOA和偏振估计方法。本文将IPNS方法扩展到双极化天线阵中，用于联合DOA和极化估计。此外，我们从理论上证明了IPNS即使在源相关的情况下仍然有效。计算机仿真结果表明，该方法能够有效地估计出源间功率差增大时的DOA和极化参数，克服了极化多信号分类（MUSIC）算法分辨率概率下降的缺点。此外，给出了该方法的性能，并给出了Cramer Rao界（CRB），其性能与极化MUSIC算法相近。
For most joint direction of arrival (DOA) and polarization estimation methods, the performances of proposed methods in dealing with unequal power sources are not discussed. However, sources with unequal powers apparently exist widely in actual applications. In this study, we propose a joint DOA and polarization estimation method for unequal power sources by utilizing the invariance property of noise subspace (IPNS) to the power of sources. This work extends the IPNS method to the dual polarized antenna array for joint DOA and polarization estimation. Moreover, we theoretically prove that the IPNS remains valid even when the sources are correlated. The computer simulations illustrate that the proposed method can effectively estimate the DOA and polarization parameters as the power difference between sources increases, as opposed to the polarimetric multiple signal classification (MUSIC) algorithm, which suffers from degradation in resolution probability. In addition, the performances of the proposed method are provided, as well the Cramer Rao Bound (CRB), which show approximate performance as the polarimetric MUSIC algorithm.
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DOI：
10.1109/aps.1991.174849
发表时间：
1991-06
期刊：
Antennas and Propagation Society Symposium 1991 Digest
影响因子：
--
作者：
Jie Li;R. T. Compton
通讯作者：
Jie Li;R. T. Compton
影响因子：
5.4
作者：
Rahamim, D;Tabrikian, J;Shavit, R
通讯作者：
Shavit, R
DOI：
10.1016/j.sigpro.2009.12.003
发表时间：
2010-06
期刊：
Signal Process.
影响因子：
--
作者：
A. Zanella;M. Chiani;M. Win
通讯作者：
A. Zanella;M. Chiani;M. Win
影响因子：
5.4
作者：
R. Levanda;Amir Leshem
通讯作者：
R. Levanda;Amir Leshem
影响因子：
5.7
作者：
Yuntao Wu;Hing Cheung So;C. Hou;Jun Li
通讯作者：
Yuntao Wu;Hing Cheung So;C. Hou;Jun Li