Closed-loop output error approaches for drone’s physics informed trajectory inference

The design of adequate countermeasures against drone's threats needs accurate trajectory estimation to avoid economic damage to the aerospace industry and national infrastructure. As trajectory estimation algorithms need highly accurate physics informed models or off-line learning algorithms, radical innovation in on-line trajectory inference is required. In this paper, a novel drone's physics informed trajectory inference algorithm is proposed. The algorithm constructs a physic informed model and infers the drone's trajectories simultaneously using a closed-loop output error architecture. Two different approaches are proposed based on a physics structure and an admittance filtering model which considers: i) full states measurements and ii) partial states measurements. Stability and convergence of the proposed schemes are assessed using Lyapunov stability theory. Simulations studies are carried out to demonstrate the scope and high inference capabilities of the proposed approach.