Minimax Game-Theoretic Approach to Multiscale H-infinity Optimal Filtering
Provides a robust filtering method for multiscale data fusion in hierarchical systems, but the improvement is incremental over existing Kalman filtering.
This work addresses multiscale data fusion in hierarchical cyber-physical systems by proposing a multiscale state-space model and a multi-stage zero-sum game to design an H∞ robust filter. Numerical experiments on 1D and 2D signals show improved signal-to-noise ratio compared to the standard Kalman filter.
Sensing in complex systems requires large-scale information exchange and on-the-go communications over heterogeneous networks and integrated processing platforms. Many networked cyber-physical systems exhibit hierarchical infrastructures of information flows, which naturally leads to a multi-level tree-like information structure in which each level corresponds to a particular scale of representation. This work focuses on the multiscale fusion of data collected at multiple levels of the system. We propose a multiscale state-space model to represent multi-resolution data over the hierarchical information system and formulate a multi-stage dynamic zero-sum game to design a multi-scale $H_{\infty}$ robust filter. We present numerical experiments for one and two-dimensional signals and provide a comparative analysis of the minimax filter with the standard Kalman filter to show the improvement in signal-to-noise ratio (SNR).