Reliable multiobject tracking is critical in numerous applications, including surveillance, autonomous navigation, and search-and-rescue, where accurate object state estimation significantly influences operational success and safety. However, distributed multi-agent tracking systems are inherently vulnerable to data modification attacks, wherein malicious agents disseminate deceptive information, severely degrading tracking accuracy. This paper addresses the challenge of detecting and mitigating data modification attacks within distributed multiobject tracking networks. We propose a resilient fusion framework that enhances the Gaussian Mixture Probability Hypothesis Density filter by integrating a Generalized Likelihood Ratio Test (GLRT)-based trust model into each agent. The GLRT continuously monitors the integrity of information received from neighboring agents, effectively identifying and discarding corrupted data, and dynamically flagging malicious entities through a Beta Trust Model. Moreover, we systematically discuss key robustification strategies designed to manage nonstationarity in the monitored statistical processes, ensuring consistent detection performance. The effectiveness of our proposed solution is demonstrated through a case study and quantitative evaluation using the Generalized Optimal Sub-Pattern Assignment (GOSPA) metric.

Automation and robotics are essential for the effective monitoring and inspection of maritime critical infrastructures and marine environments. We demonstrate the use of an unmanned surface vehicle, integrated with acoustic and optical sensors, to perform fast and accurate inspections of maritime infrastructures in confined areas (i.e., presence of buildings and multiple obstacles). High resolution maps are obtained fusing offline data from LiDAR and 2D acoustic multi-beam forward looking camera point clouds. The technological pipeline is demonstrated through a survey in Copenhagen harbour. The data acquisition leverages on methods to improve path planning and localization to correct failures in the RTK GNSS due to shadowing of buildings and other obstacles. The entire data flow is streamlined to produce fast delivery time and data access, optimizing data acquisition and processing, providing results into a dedicated web service tailored to users’ needs for knowledge and information extraction.