Parameter Identification of a PN-Guided Incoming Missile Using an Improved Multiple-Model Mechanism

Abstract

To accurately estimate the state of the incoming missile and effectively implement an evasion maneuver, the parameters of the incoming missile, including a guidance constant and a first-order lateral time constant, should be identified online. To this end, assuming that a missile with proportional navigation (PN) guidance law attempts to attack an aerial target with bang-bang evasion maneuvers, a parameter identification model based on the gated recurrent unit (GRU) neural network is built in this paper. The analytic identification solutions for the guidance law parameter and the first-order lateral time constant are derived and show that the identification of the latter parameter is more difficult. The inputs of the identification model are available kinematic information between the aircraft and the missile, while the outputs contain the regression results of the missile's parameters. To increase the training speed and identification accuracy of the model, an output processing method called the improved multiple-model mechanism (IMMM) is proposed in this paper. The effectiveness of IMMM and the performance of the established model are demonstrated through numerical simulations under various engagement scenarios.