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

Abstract

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 privacy of interaction data during node exchange in sensor networks. Scaled uniform quantization is introduced 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 function, and the quantization level. The desired gain matrix is then solved recursively. Finally, the effectiveness of the proposed filtering algorithm is demonstrated through a three-tank simulation example.

Keywords

Distributed filtering / recursive filtering / privacy protection / uniform quantization / output mask

References

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