Data Fusion Assurance for the Kalman Filter in Uncertain Networks
Bonnie Zhu, S. Shankar Sastry · 2008
Due to standardization and connectivity to other networks, networked control systems, a vital component of many nations' critical infrastructures, face potential disruption. Its possible manifestation can affect Kalman filter, the primary recursive estimation method used in control engineering field. Whereas to improve such estimation, data fusion may take place at a central location to fuse and process multiple sensor measurements delivered over the network. In a uncertain networked control system where the nodes and links are subject to attacks, false or compromised or missing individual readings can produce skewed result. To assure the validity of data fusion, this paper proposes a centralized trust rating system that evaluates the trustworthiness of each sensor reading on top of the fusion mechanism. The ratings are represented by Beta distribution, the conjugate prior of the binomial distribution and its posterior. Then an illustrative example demonstrates its efficiency.