Target tracking with a dynamic and adaptive selection of radars based on entropy

• Main Authors: Chunxia Li, De Zhang, Jianjun Ge, Wujun Wang
• Format: Article
• Language: English
• Published: Wiley 2019-09-01
• Series: The Journal of Engineering
• Subjects: entropy; target tracking; object detection; sensor fusion; different time index; information theory; target information; multiple radars; given entropy; target motion state; fusion tracking method; dynamic selection; adaptive selection; modern rapidly changing battlefield; multiple objects multiple missions; different radars
• Online Access: https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0677

In the modern rapidly changing battlefield, the resources of multiple radars are usually limited for multiple objects multiple missions. Moreover, the amount of target information acquired by different radars at different time index is different. Thus, as to target tracking, it is important to reasonably and dynamically allocate radars’ resources. In the study, based on information theory, the entropy is utilised to quantitatively measure the amount of target information observed from multiple radars. Corresponding, the lower bound (LB) of the given entropy is also derived. The smaller the value of the entropy, the more accurate the estimate of target motion state is. So based on minimising the entropy LB of target information acquired from radars at different times, a new fusion tracking method is proposed to dynamically adaptively choose radars with high amount of target information for target tracking. The simulation results show that the proposed method has higher tracking accuracy than the fusion tracking without optimal choice of radars.