Sparsity-Aware Sensor Selection for Privacy-Preserving Zonotopic Fusion Filtering in Cloud-Based Vehicle Tracking Systems

Abstract

This article investigates the zonotopic filtering problem for cloud-based vehicle tracking systems under joint privacy and communication bandwidth constraints. Cloud-side tracking platforms improve vehicle state estimation accuracy by fusing multisource measurements collected from roadside nodes. However, during roadside-to-cloud data transmission, the system is confronted with coupled challenges arising from privacy leakage risks and the communication burden induced by concurrent data uploads from large-scale sensor deployments. To address these challenges, a zonotopic fusion filtering framework incorporating privacy-preserving mechanisms and sparsity-aware sensor selection strategies is proposed to achieve a balanced tradeoff among privacy protection, communication efficiency, and estimation accuracy. First, a novel secret-sharing-based zonotopic fusion filtering method is developed, which embeds a dynamic-encoding-based secret sharing mechanism into the multisensor fusion process to protect both transmitted data and estimation results. Furthermore, to reduce redundant communications, a sparsity-promoting sensor selection scheme is constructed by introducing a sparsity penalty into the filter parameter optimization problem, enabling transmission only from sensors that effectively contribute to the current estimation accuracy. The resulting optimization problem is solved using convex relaxation and the alternating direction method of multipliers, yielding analytical update expressions for the filter parameters. In addition, the boundedness of the vehicle state estimation error is rigorously analyzed, and a sufficient condition ensuring that the estimation error remains bounded is established. Finally, simulation experiments demonstrate the effectiveness of the proposed algorithm in achieving accurate, communication-efficient, and privacy-preserving state estimation.