Privacy-preserving push-sum distributed cubature information filter for nonlinear target tracking with switching...

Area of Science:

• Control Theory
• Information Theory
• Cybersecurity

Background:

• Distributed state estimation faces data security challenges, especially in sensitive nonlinear target tracking scenarios.
• Restricted communication environments complicate accurate and secure estimation.
• Existing methods may compromise privacy or accuracy under adversarial conditions.

Purpose of the Study:

• To develop a novel consensus-based cubature information filtering algorithm for secure distributed state estimation.
• To enhance privacy preservation in nonlinear target tracking without sacrificing global estimation accuracy.
• To address communication constraints in distributed systems.

Main Methods:

• A privacy-preserving approach using state decomposition is employed.
• The algorithm utilizes a push-sum consensus mechanism.
• The distributed approach is extended to handle switching directed topologies.

Main Results:

• Theoretical proofs demonstrate the algorithm's average convergence, privacy preservation, and stability.
• Simulations validate the algorithm's performance in terms of security, accuracy, and robustness.
• The method effectively protects local information from adversaries.

Conclusions:

• The proposed algorithm offers a secure and accurate solution for distributed state estimation under communication constraints.
• It provides a feasible approach for target tracking in environments with privacy and communication limitations.
• The method enhances the robustness of distributed systems against adversaries.