Tracking the Warhead Among Objects Separation from the Reentry Vehicle in a Clear Environment

Defence Science Journal

Cheng-Yu Liu¹ 

[1] Lee-Ming Institute of Technology, Taipei

关键词: Input estimation;    probabilistic data assocition filter;    extended Kalman filter;    tracking algorithm;    trajectory estimation;   

DOI  :  

学科分类：社会科学、人文和艺术（综合）

来源: Defence Scientific Information & Documentation Centre

【 摘 要 】

Separating a reentry vehicle into warhead, main body, and debris is a conventional and efficient means of producing a huge decoy and increasing the kinetic energy of the warhead. This procedure causes the radar to track the main body and debris, which radar cross section are large, and ignore the warhead, is the most important part of the reentry vehicle. The warhead is difficult to identify after separation using standard tracking criteria. This study presents a novel tracking algorithm by integrating input estimation and modified probabilistic data association filter to identify warhead among objects separation from the reentry vehicle in a clear environment. The proposed algorithm provides a good tracking capability for the warhead ignoring the radar cross section. Simulation results reveal that the errors between the updated and warhead trajectories are reduced to a small interval in a short time. Therefore, the radar can generate a beam to illuminate the right area and keep tracking the warhead all the time. This algorithm is worthy of further study and application. Defence Science Journal, 2009, 59(2), pp.113-125 , DOI:http://dx.doi.org/10.14429/dsj.59.1498

【 授权许可】

Unknown