Improved IMM algorithm based on support vector regression for UAV tracking  

作　　者：ZENG Yuan LU Wenbin YU Bo TAO Shifei ZHOU Haosu CHEN Yu 

机构地区：[1]Shanghai Spaceflight Electronic and Communication Equipment Research Institute,Shanghai 201109,China [2]Science and Technology on Near-Surface Detection Laboratory,Wuxi 214035,China [3]School of Electronic and Optical Engineering,Nanjing University of Science and Technology,Nanjing 210094,China

出　　处：《Journal of Systems Engineering and Electronics》2022年第4期867-876,共10页系统工程与电子技术（英文版）

基　　金：supported by the Foundation of Key Laboratory of Near-Surface。

摘　　要：With the development of technology, the relevant performance of unmanned aerial vehicles(UAVs) has been greatly improved, and various highly maneuverable UAVs have been developed, which puts forward higher requirements on target tracking technology. Strong maneuvering refers to relatively instantaneous and dramatic changes in target acceleration or movement patterns, as well as continuous changes in speed,angle, and acceleration. However, the traditional UAV tracking algorithm model has poor adaptability and large amount of calculation. This paper applies support vector regression(SVR)to the interacting multiple model(IMM) algorithm. The simulation results show that the improved algorithm has higher tracking accuracy for highly maneuverable targets than the original algorithm, and can adjust parameters adaptively, making it more adaptable.

关 键 词：interacting multiple model(IMM)filter constant acceleration(CA) unmanned aerial vehicle(UAV) support vector regression(SVR) 

分 类 号：V279[航空宇航科学与技术—飞行器设计] TP18[自动化与计算机技术—控制理论与控制工程]