Modelo de integración de información contextual y radar en sistemas neuronales para seguimiento de maniobras de combate aéreo

Abstract

Air surveillance and traffic control radar tracking systems present a variety of known problems related to uncertainty and lack of accurately in radar measurements, used as source in these systems. In this work, we feature the theoretical aspects of a tracking algorithm based on neural network paradigm where, from discrete measurements provided by surveillance radar, the objective will be to estimate the target state for tracking purposes as accuracy as possible. The absence of an optimal statistical solution makes the featured neural network attractive despite the availability of complex and well-known filtering algorithms. Neural networks exhibit universal mapping capabilities that allow them to be used as a control tool for capturing hidden information about models learned from a dataset. We use these capabilities to let the network learn, not only from the received radar measurement information, but also from the aircraft maneuvering context, contextual information, where tracking application is working, taking into account this new contextual information which could be obtained from predefined, commonly used, and well-known aircraft trajectories. In this case study, the described solution is applied to a typical air combat maneuvering, a dogfight, a form of aerial combat between fighter aircraft. Advantages of integrating contextual information in a neural network tracking approach are demonstrated. The scheme described as a solution to the tracking problem in maneuvering trajectories should be considered as a proposal that could be used in several environments where contextual information could help in the tracking problem to be solved.