Zonotopic Distributed Fusion Filtering for 2-D Nonlinear Systems Over Sensor Networks: A Channel-Based Bit Rate Constraint - Yang, Chengyu; Liang, Jinling; Zhao, Zhongyi (IEEE transactions on signal processing, 2025)
This paper investigates the distributed fusion filtering problem for a class of two-dimensional nonlinear systems subject to unknown-but-bounded noises over sensor networks using the zonotopic set-membership approach. Distinct from the existing studies, a novel channel-based bit rate constraint model associated with a binary encoding scheme is introduced to characterize the limited bandwidth of sensor networks, where the length of the binary code sequences in communication channels among sensors is directly influenced by the limited bit rate. The objective is to design a distributed fusion filter which can effectively estimate the system states and construct a zonotope which encloses the overall filtering error. To this end, multiple local filters are developed, and the corresponding zonotopes that respectively bound the local filtering errors and the encoding errors are derived using set operation techniques. By minimizing the F-radius of these zonotopes, the locally optimal filter gains and the optimal channel bit rate allocation strategy are obtained. Subsequently, the fused estimation is generated by integrating these local estimations with appropriately determined fusion weights. Finally, the effectiveness of the proposed filtering algorithm is validated through a numerical example.