Decentralised Multi-agent Search, Track and Defence Coordination using a PMBM filter and Data-driven Robust Optimisation

Abstract: In an air defence scenario decisions need to be taken with extreme precision and under high pressure. These decisions becomes even more challenging when the aircraft in question need to function as a team and coordinate their effort. Because of the difficulty of the task, and the amount of information that needs to be rapidly processed, fighter pilots can benefit greatly from computer-assisted decision making.  In this thesis this kind of decentralised multi-agent coordination problem is studied and mission assignment models, based on robust and stochastic optimisation, are evaluated. Since the information obtained by aircraft sensors often suffer from a notable amount of noise and the scenario state therefore is uncertain, a Poisson multi-Bernoulli mixture filter is implemented in order to model these noisy measurements and keep track of potential adversaries. The study finds that the filter used was more than capable of handling the scenario uncertainties and provided valuable task information to the mission assignment models. However, the preliminary robust optimisation models based entirely on the positional uncertainty of the adversaries were not sophisticated enough for such a complex coordination problem, indicating that further research is needed in this area.