State estimation in TPN and PPN guidance laws by using Unscented and Extended Kalman filters

Abstract

In this paper two important strategies of proportional navigation guidance law, i.e. TPN and PPN, considering highly nonlinear model of missile and target engagement are simulated for a tactical homing missile (air to air). It is supposed that target has an unknown constant acceleration with random starting time. With considering process noise and measurement noise for the system, both guidance laws TPN and PPN are simulated by applying Unscented Kalman Filter (UKF). We consider flight control system dynamics in the guidance system and model them as a single-lag and then both guidance laws are simulated by applying UKF. At the end, obtained results from applying UKF are compared to the well known Extended Kalman Filter (EKF). Our Simulation has shown that UKF, in comparison with EKF, has much better performance in state estimation and reducing the effect of noise on the missile command acceleration.