Efficient multiple hypothesis track processing of boost-phase ballistic missiles using IMPULSE©-generated threat models

Abstract

In this thesis, a multiple hypotheses tracking (MHT) algorithm is developed to successfully track multiple ballistic missiles within the boost phase. The success of previous work on the MHT algorithm and its application in other scientific fields enables this study to realize an efficient form of the algorithm and examine its feasibility in tracking multiple crossing ballistic missiles even though various accelerations due to staging are present. A framework is developed for the MHT, which includes a linear assignment problem approach used to search the measurement-to-contact association matrix for the set of exact N-best feasible hypotheses. To test the new MHT, an event in which multiple ballistic missiles have been launched and threaten the North American continent is considered. To aid in the interception and destruction of the threat far from their intended targets, the research focuses on the boost-phase portion of the missile flight. The near-simultaneous attacks are detected by a network of radar sensors positioned near the missile launch sites. Each sensor provides position reports or track files for the MHT routine to process. To quantify the performance of the algorithm, data from the National Air and Space Intelligence Center's IMPULSE ICBM model is used and demonstrates the feasibility of this approach. This is especially significant to the U.S. Missile Defense Agency since the IMPULSE model represents the cognizant analyst's accurate representation of the ballistic threats in a realistic environment. The results show that this new algorithm works exceptionally well in a realistic environment where complex interactions of missile staging, non-linear thrust profiles and sensor noise can significantly degrade the track algorithm performance especially in multiple target scenarios.