.png){kind=icon}
返回
Graph-based minimum error entropy Kalman filtering
.png){kind=icon}
PRE
AI
DOI：10.1016/j.sigpro.2024.109535.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
When the Gaussian kernel function is chosen with a small kernel bandwidths, the minimum error entropy Kalman filter (MEEKF) exhibits excellent performance. However, further narrowing the kernel bandwidths leads to a degradation in performance. To address this issue, this paper proposed a Graph-based MEEKF (G-MEEKF) algorithm based on Graph Signal Processing (GSP) theory. The G-MEEKF algorithm addresses the problem by smoothing the errors using graph filtering and incorporating graph topology into the cost function. The convergence of the algorithm is theoretically analyzed and simulations demonstrate that G-MEEKF improves the performance of MEEKF under small kernel bandwidths. Furthermore, it is emphasized that the topology of the graph abstracted from the minimum error entropy (MEE) definition also influences the performance of the algorithm.
Keyword:
Kalman filter
Minimum error entropy
Small bandwidths
Graph Signal Processing
期刊
IF:
3.6
论文数:
10.0K
被引数:
1.7W
机构
暂无机构信息
引用论文
分享收藏Robust M-estimation-based maximum correntropy Kalman filter基于鲁棒M估计的最大相关熵Kalman滤波器.png)
ISA TRANSACTIONS
ISA TRANSACTIONSIF6.5
PREAI
分享收藏分享收藏
查看更多内容