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Generalised maximum complex correntropy-based DOA estimation in presence of impulsive noise
IET Radar Sonar and Navigation ( IF 1.7 ) Pub Date : 2020-05-18 , DOI: 10.1049/iet-rsn.2019.0400
Fuqiang Ma 1, 2, 3 , Hongying Bai 2, 4 , Xiaotong Zhang 1, 2, 3 , Cheng Xu 2 , Yiping Li 2
Affiliation  

Most existing methods for direction-of-arrival (DOA) estimation are severely influenced by impulsive noise due to their Gaussian noise assumption. As a typical non-linear similarity measure, the maximum correntropy criterion (MCC) has been considered to restrain impulsive noise, because of its ability to exploit the high-order statistics of signal. However, Gaussian kernel-based MCC method is not always the optimal choice and is only suitable for the real-valued signal, which certainly limits its applications. To solve the aforementioned problems, in this study, the authors proposed a novel generalised maximum complex correntropy criterion (GMCCC)-based complex-valued quasi-Newton method to restrain impulsive noise. GMCCC adopts the generalised complex Gaussian density function as the kernel function with more flexible parameters. Besides, it can extend the benefit to the complex-valued signal. Furthermore, its properties are formalised. The complex-valued quasi-Newton method guarantees the positive definite Hessian matrix to achieve the alternate minimisation of signal subspace and signal matrix. GMCCC achieves the accurate DOA estimation from the received data which does not require the covariance matrix. Stability performance and convergence are analysed. Experiment results show that the proposed GMCCC algorithm possesses the robustness and outperforms the state-of-the-art algorithms.

中文翻译:

脉冲噪声存在下基于广义最大复杂熵的DOA估计

由于存在高斯噪声假设,因此大多数现有的到达方向(DOA)估计方法都会受到脉冲噪声的严重影响。作为一种典型的非线性相似性度量,由于其具有利用信号的高阶统计量的能力,已考虑将最大熵准则(MCC)抑制脉冲噪声。但是,基于高斯核的MCC方法并不总是最佳选择,仅适用于实值信号,这无疑限制了其应用。为了解决上述问题,在这项研究中,作者提出了一种新的基于广义最大复数熵准则(GMCCC)的复值拟牛顿法来抑制脉冲噪声。GMCCC采用广义复高斯密度函数作为具有更灵活参数的核函数。除了,它可以将好处扩展到复数值信号。此外,其属性已正式确定。复值拟牛顿法保证了正定的Hessian矩阵可以实现信号子空间和信号矩阵的交替最小化。GMCCC从不需要协方差矩阵的接收数据中获得准确的DOA估计。分析了稳定性能和收敛性。实验结果表明,所提出的GMCCC算法具有鲁棒性,并且优于最新的算法。GMCCC从不需要协方差矩阵的接收数据中获得准确的DOA估计。分析了稳定性能和收敛性。实验结果表明,所提出的GMCCC算法具有鲁棒性,并且优于最新的算法。GMCCC从不需要协方差矩阵的接收数据中获得准确的DOA估计。分析了稳定性能和收敛性。实验结果表明,所提出的GMCCC算法具有鲁棒性,并且优于最新的算法。
更新日期:2020-05-18
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