Maximum likelihood array calibration using particle swarm optimisation
Maximum likelihood array calibration using particle swarm optimisation
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DOI：
10.1049/iet-spr.2011.0133
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发表时间：
2012-09
期刊：
影响因子：
--
通讯作者：
Shuang Wan;Pei-Jung Chung;B. Mulgrew
中科院分区：
文献类型：
--
作者：
Shuang Wan;Pei-Jung Chung;B. Mulgrew
阵列形状误差的校正是现有大多数信源定位算法的关键问题。本文采用无条件最大似然(UML)估计器进行远场自标定和近场导频标定，其目标函数用粒子群算法(PSO)进行优化。本文提出了一种新技术--衰减对角线加载(DDL)，通过动态降低PSO在高信噪比(SNR)下的性能来提高PSO的性能，这是基于一种反直观的观察，即在低信噪比时，UML线性目标函数的全局最优性更加突出。数值仿真结果表明，该算法对较大的形状误差具有较强的鲁棒性，具有较高的精度，且不存在初始化问题。此外，DDL技术可以与不同的全局优化算法相结合，以提高性能。数学分析表明，该方法适用于任何使用了统一建模语言估计器的阵列处理问题。
Calibration of array shape error is a key issue for most existing source localisation algorithms. In this study, the far-field self-calibration and near-field pilot-calibration are carried out using unconditional maximum likelihood (UML) estimator whose objective function is optimised by particle swarm optimisation (PSO). A new technique, decaying diagonal loading (DDL), is proposed to enhance the performance of PSO at high signal-to-noise ratio (SNR) by dynamically lowering it, based on the counter-intuitive observation that the global optimum of the UML objective function is more prominent at lower SNR. Numerical simulations demonstrate that the UML estimator optimised by PSO with DDL is robust to large shape errors, optimally accurate and free of the initialisation problem. In addition, the DDL technique can be coupled with different global optimisation algorithms for performance enhancement. Mathematical analysis indicates that the DDL is applicable to any array processing problem where the UML estimator is employed.