Cooperative Target Tracking

Cooperative target tracking focuses on improving the accuracy and robustness of tracking multiple targets by integrating information from multiple sensors or agents. Current research emphasizes distributed algorithms, such as Kalman filtering variants and auction-based approaches, often incorporating sensor fusion techniques and machine learning for improved feature extraction and prediction. This field is crucial for applications like autonomous driving, robotics, and surveillance, where reliable multi-target tracking in challenging environments is essential, driving advancements in both theoretical understanding and practical system design.