Multi-Rate Sampled-Data Secure Fusion Estimation Against Malicious Hybrid Attacks

This paper investigates the Kalman fusion estimation problem for multi-sensor systems based on multi-rate sampled data within a non-secure network environment. For each sensor, an innovative multi-rate sampling estimation module is proposed, allowing for multiple samplings within a single estimation cycle to gather as much sampled information as possible. The sampled data during transmission is thought to encounter three potential scenarios: being subjected to DoS attack, FDI attack, or undergoing normal transmission. These three potential scenarios are modeled as a random phenomenon described by two sets of Bernoulli variables. A unified information framework is subsequently introduced, adept at encompassing the three attack scenarios along with the multi-rate sampling process. This framework serves as the basis for the design of a local secure Kalman estimator, followed by stability analysis. Finally, a distributed secure fusion estimation algorithm is proposed, and its effectiveness is demonstrated through a simulation example.

• Publication year

  2025

• Venue

  IEEE Transactions on Signal and Information Processing over Networks

• Publication date

  Unknown publication date

• Fields of study

  Computer Science, Engineering

• Identifiers
  DOI 10.1109/TSIPN.2025.3559434
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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