Modeling on Ballistic Missile Threat Assessment

Abstract

Ballistic Missile (BM) threat assessment is the chief problem of Ballistic Missile Defense System (BMDS) assistant decision-making. Firstly, the BM threat assessment model is constructed through qualitative analysis and quantitative analysis on threat assessment indexes influencing the threat assessment degree; then Improved Clonal Selection Algorithm Optimizing Neural Network (ICLONALG-NN) is designed to solve the model through introducing crossover operator and population update operator into traditional CLONALG to optimize Neural Network parameters. Experimental simulation confirms the superiority and practicability of assistant decision-making model solved by ICLONALG-NN.