Privacy-Preserving Distributed Recursive Filtering for State-Saturated Systems with Quantization Effects

This paper addresses the problem of distributed recursive filtering for state-saturated systems in a networked communication environment. An output mask function is employed to safeguard the pri- vacy of interaction data during node exchange in sensor networks. Scaled uniform quantization is intro- duced to facilitate the digital communication and optimize the network resource usage. The primary objective of the study is to design a distributed recursive filter that ensures the filtering error covariance remains bounded over a finite horizon. Specifically, by using Riccati-like equations, an upper bound for the filtering error covariance is derived, which depends on the network topology, the output mask func- tion, and the quantization level. The desired gain matrix is then solved recursively. Finally, the effective- ness of the proposed filtering algorithm is demonstrated through a three-tank simulation example.