电子学报2018，Vol.46Issue(4)：886-894,9.DOI:10.3969/j.issn.0372-2112.2018.04.017
基于自控蝙蝠算法智能优化粒子滤波的机动目标跟踪方法
Adaptive Control Bat Algorithm Intelligent Optimization Particle Filter for Maneuvering Target Tracking
摘要
Abstract
Resampling of particle filters will cause particle depletion and the comprehensive performance is low,which can hardly meet the requirement of high frequency accurate radar.To address the problem,a novel adaptive control bat algorithm optimized particle filter for maneuvering target tracking was proposed in this paper.It introduced bat algorithm into particle filter and took particle as bat individual to simulate the process of hunting and made particles move to high likelihood area.Meanwhile,by taking proportion of accepting as feedback,the improved algorithm designed closed-loop control strategy and controlled the balance between ability of global optimization and local optimization and improved rationality of particles distribution and accuracy of filter.Finally,the improved algorithm was tested in basic nonlinear filter model and strong maneuvering-jamming target tracking model.The experimental results prove that the new algorithm conduces to enhancement of the precision for target tracking.关键词
粒子滤波/蝙蝠算法/粒子多样性/闭环控制/目标跟踪Key words
particle filter/bat algorithm/particle diversity/closed-loop control/target tracking分类
信息技术与安全科学引用本文复制引用
陈志敏,吴盘龙,薄煜明,田梦楚,岳聪,顾福飞..基于自控蝙蝠算法智能优化粒子滤波的机动目标跟踪方法[J].电子学报,2018,46(4):886-894,9.基金项目
国家自然科学基金(No.61501521,No.U1330133,No.61473153) （No.61501521,No.U1330133,No.61473153）
中国博士后科学基金(No.2015M582861) （No.2015M582861）
