Machine Learning of Behavioural Models for Improved Multi-Sensor Fusion

SSensor fusion is increasingly critical in the defense and security sectors, enhancing decision-making by providing operators with integrated data from diverse sources. This capability is particularly valuable on platforms such as unmanned aerial vehicles (UAVs), where it enables less sophisticated sensors to collectively emulate the functionality of more advanced systems, thereby reducing costs and enhancing resilience. The efficacy of sensor fusion heavily relies on the accuracy of behavioral models used to predict the movements of surveillance targets. Current models, predominantly based on simplistic, nearly constant velocity assumptions, fall short in addressing complex behaviors and deception tactics used by targets.
This PhD research aims to address these shortcomings by developing advanced behavioral models through machine learning techniques, utilizing asynchronous, historic multi-sensor data. Despite the potential of machine learning in this context, its application has been limited due to the complexity of dynamically modeling target behavior while managing non-linear measurements and handling detection anomalies. Recent advancements in computational techniques, such as extensions to Particle Markov Chain Monte Carlo (Particle-MCMC) that utilize gradient information and leverage modern deep learning compute resources, offer promising solutions to these challenges.
This project will explore and adapt these cutting-edge techniques to improve the predictive accuracy of behavioral models derived from historic surveillance data. The research will assess the utility of these models within the framework of generic multi-sensor fusion and in scenarios specific to Leonardo-a leading entity in defense and security-potentially encompassing the integration of electronic surveillance, radar, and electro-optical/infrared (EO/IR) data. Success in this endeavor could significantly advance multi-sensor data fusion research, bolstering Leonardo's capabilities and positioning the researcher as a future leader in data science within a vital sector committed to national security.