Information processing and resource allocation in intelligent radar systems against ballistic objects

Abstract

In this dissertation, we have covered the results of some useful techniques for constructing a weapon control system for future ballistic missile defense systems. The future ballistic missile defense systems will focus on how effectively the defender can track and intercept a large number of simultaneous missile attacks. In order to accomplish this, first, the sensor resource assignment problem through automatic target handover is solved by Mixed Integer Linear Programming (MILP) and compared with the First-In-First-Out greedy algorithm as of heuristic technique to achieve seamless target tracking in networked radar systems. Secondly, for the effective trajectory and impact point prediction, which are the prerequisite of the previous task, we applied the well-known machine learning techniques that were not considered in the past as of model-free approach. The Recurrent Neural Network (RNN) and the Long-Short Term Memory (LSTM) method, which is suitable for time series data processing, were considered for trajectory and impact point estimation. The applicability of the technique is verified by using a simulation of the ballistic missile trajectory generator. In the appendix, a concept of distributed command system is established and its applicability was verified by the heuristic weapon-target assignment algorithm for the low layer air defense systems to be constructed.