Parameter Identification of a PN-Guided Incoming Missile Using an Improved Multiple-Model Mechanism
This work addresses the need for accurate missile parameter identification in active defense systems for aerial targets, representing an incremental improvement in existing methods.
The paper tackles the problem of identifying guidance law parameters and lateral time constants of an incoming missile using a GRU neural network, achieving improved training speed and identification accuracy through a proposed improved multiple-model mechanism (IMMM) as validated in numerical simulations.
An active defense against an incoming missile requires information of it, including a guidance law parameter and a first-order lateral time constant. To this end, assuming that a missile with a proportional navigation (PN) guidance law attempts to attack an aerial target with bang-bang evasive 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. The inputs of the identification model are available kinematic information between the aircraft and the missile, while the outputs contain the regression results of missile parameters. To increase the training speed and the identification accuracy of the Model, an output processing method called improved multiplemodel 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.