Complex-Valued Gaussian Sum Filter for Nonlinear Filtering of Non-Gaussian/Non-Circular Noise
Complex-Valued Gaussian Sum Filter for Nonlinear Filtering of Non-Gaussian/Non-Circular Noise
复制标题
DOI：
10.1109/lsp.2014.2361459
复制
发表时间：
2015-04
影响因子：
3.9
通讯作者：
Arash Mohammadi;K. Plataniotis
中科院分区：
文献类型：
--
作者：
Arash Mohammadi;K. Plataniotis
考虑到高斯和滤波器（GSF）和多模型自适应估计（MMAE）方法在正确（圆形）高斯信号假设无效的情况下的应用，本文提出了一种新的复值高斯和滤波器（C/GSF）用于非高斯/非圆形测量噪声的非线性滤波。尽管使用GSF进行递归状态估计的文献非常丰富，但其包含系统的完整二阶统计量并能处理非高斯/非圆测量的复值对偶尚未在文献中进行研究。本文解决了这一差距。C/GSF是一种计算上有吸引力的自适应滤波器，其中利用改进的贝叶斯学习技术控制非圆形高斯分量的数量，该技术用于将得到的非高斯和混合物折叠成等效的复值高斯项。仿真结果表明，与传统算法相比，C/GSF具有显著的性能改进。
Motivated by application of Gaussian sum filters (GSF) and multiple model adaptive estimation (MMAE) approaches in scenarios where assumption of proper (circular) Gaussian signals is not valid, the letter proposes a novel complex-valued Gaussian sum filter (C/GSF) for non-linear filtering of non-Gaussian/non-circular measurement noise. Although the literature on recursive state estimation using GSF is rich, its complex-valued counterpart which incorporates the full second-order statistics of the system and can cope with non-Gaussian/non-circular measurements, has not yet been investigated in the literature. The paper addresses this gap. The C/GSF is a computationally attractive adaptive filter where the number of non-circular Gaussian components is controlled utilizing a modified Bayesian learning technique which is used to collapse the resulting non-Gaussian sum mixture into an equivalent complex-valued Gaussian term. Simulation results indicate that the C/GSF provides significant performance improvement over its traditional counterparts.