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

TL;DR

This paper develops a neural network-based model to identify key missile parameters during an attack, enhancing accuracy and speed with an improved multiple-model mechanism, aiding active missile defense systems.

Contribution

It introduces a GRU neural network model combined with IMMM for rapid and accurate missile parameter identification during evasive maneuvers.

Findings

The model accurately estimates guidance law parameters.

IMMM improves training speed and identification accuracy.

Numerical simulations validate effectiveness across scenarios.

Abstract

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.