Abstract: 

Recent advancements in sensor networks, multi-agent systems, and distributed computing have spurred significant interest in consensus-driven distributed target tracking (CDTT). This paradigm leverages collaborative estimation and decision-making among decentralized agents to achieve robust, scalable, and resilient tracking in dynamic environments. Applications span autonomous systems, surveillance, robotics, IoT, and defense. However, challenges remain in improving estimation accuracy, handling communication constraints, ensuring security, and integrating AI-driven techniques.

This Special Issue invites high-quality contributions on theoretical, algorithmic, and practical advancements in CDTT. Topics include but are not limited to: Distributed consensus algorithms for state estimation and data fusion, Resilient tracking strategies against adversarial attacks or network failures, Communication-efficient protocols (e.g., event-triggered, quantized, or compressed sensing), Heterogeneous multi-agent cooperation, and Machine learning-enhanced methods (e.g., federated learning, reinforcement learning).

Session Chairs: 

Prof. Tiancheng Li (Northwestern Polytechnical University, China) , Assoc. Prof. Guchong Li (Northwestern Polytechnical University, China),  

Prof. Giorgio Battistelli (University of Florence, China), 

Prof. Juan M Corchado (University of Salamanca, China)