Work

Event-Triggered Robust Fusion Estimation for Multi-Sensor Systems Under Random Packet Drops

Year: 2026

Type: article

Abstract: This paper focuses on the design of robust fusion estimators for multi-sensor systems experiencing constrained communications, model uncertainties, and random packet dropouts. To mitigate the impact of modeling errors, a sensitivity-penalized robust state estimator is employed at each local estimator. At the local fusion estimators, a centralized robust fusion estimation algorithm is derived by improving the cost function of the sensitivity-penalized estimator. The implementation of an event-triggered strategy effectively alleviates the burden on the communication channels linking the sensors and the fusion center. Moreover, the fusion estimator is capable of handling packet drops caused by unreliable communication channels, and the pseudo cross-covariance matrix is accordingly formulated. Sufficient conditions are derived to ensure the uniform boundedness of the estimation error for the proposed robust fusion estimator. Finally, simulation experiments using a tractor-car system validate the performance and advantages of the presented algorithm.

Source: Signals

DOI: 10.3390/signals7010009

Authors: Shaoxun Lu, Huabo Liu

Institution: Qingdao University

Language: English

Topic: Target Tracking and Data Fusion in Sensor Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,207

Funders: National Natural Science Foundation of China, Natural Science Foundation of Shandong Province