Work

Distributed Fusion Filtering with Prediction Compensation for Multi-Sensor Systems Subject to DoS-Attack-Induced Packet Dropouts

Year: 2026

Type: article

Abstract: This paper investigates the distributed fusion estimation problem for multi-sensor cyber-physical systems (CPSs), where the communication channels from local estimators to the fusion center are subject to random packet dropouts. Packet dropouts induced by either network congestion or intermittent denial-of-service (DoS) attacks are modeled as Bernoulli random variables. When a local estimate is lost, a prediction compensation strategy is adopted at the fusion center, where the missing data are replaced by their one-step predictors. By constructing an augmented state consisting of the original state, local prediction errors, and virtual measurements, the multi-sensor system is transformed into a stochastic system with random parameter matrices and one-step autocorrelated noises. Based on the transformed system, a distributed state fusion (DSF) filter is proposed via the innovation analysis method, whose filter gain depends on the successful-reception probabilities. The stability of the proposed DSF filter is analyzed, and a sufficient condition for the existence of a steady-state filter is obtained. The steady-state gain can be pre-computed offline, thereby reducing the online computational burden. Simulation results validate the effectiveness of the proposed algorithm.

Source: Sensors

DOI: 10.3390/s26175633

Authors: Fengtao Hu, Jing Ma

Institution: Heilongjiang University

Language: English

Topic: Target Tracking and Data Fusion in Sensor Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,137