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Graph-based minimum error entropy Kalman filtering

delete2024-09-01
delete3
PRE
AI
K
Kun Zhang
G
Gang Wang *
Y
Yuzheng Zhou
J
Jiacheng He
X
Xuemei Mao
B
Bei Peng
DOI：10.1016/j.sigpro.2024.109535delete
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Abstract

Abstract

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.
Keywords:
Kalman filter
Minimum error entropy
Small bandwidths
Graph Signal Processing

Journal

Signal Processing cover
Signal Processing
IF:
3.6
Papers:
9.9K
Citations:
1.7W

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