Hierarchical Average Fusion With GM-PHD Filters Against FDI and DoS Attacks

We address the multisensor multitarget tracking problem based on a hierarchical sensor network. In this setup, there is a fusion center, several cluster heads, and many sensors. Each sensor runs a Gaussian mixture probability hypothesis density (PHD) filter. The sensors send their locally calculated...

Full description

Saved in:
Bibliographic Details
Published in:IEEE signal processing letters 2024, Vol.31, p.934-938
Main Authors: Yang, Hao, Li, Tiancheng, Yan, Junkun, Elvira, Victor
Format: Article
Language:English
Subjects:
Algorithms
average fusion
Behavioral sciences
Clusters
Denial of service attacks
Denial-of-service attack
Density of states
Electronic mail
Fake news
Information filters
Multiple target tracking
network attack
Pediatrics
PHD filter
Radio frequency
Sensors
Tracking problem
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We address the multisensor multitarget tracking problem based on a hierarchical sensor network. In this setup, there is a fusion center, several cluster heads, and many sensors. Each sensor runs a Gaussian mixture probability hypothesis density (PHD) filter. The sensors send their locally calculated Gaussian components to the local cluster head in the presence of false data injection (FDI) and denial-of-service (DoS) attackers. In this letter, we propose a hybrid PHD averaging fusion framework that consists of two parts: one uses the arithmetic average (AA) fusion to compensate for information shortage due to DoS and the second one uses the geometric average (GA) fusion to suppress false information due to FDI. By integrating the respective zero forcing and avoiding behaviors of the two average fusion approaches, our proposed hybrid fusion scheme is proven resilient to both FDI and DoS attacks. Experimental results illustrate that our proposed algorithm can provide reliable tracking performance against FDI and DoS attacks.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2024.3356823