摘要
为应用自主移动机器人搜寻未知放射性物体,本文基于递推贝叶斯估计模型提出了一种改进粒子滤波的放射源定位方法。首先,建立初始粒子集,并根据观测值对粒子权值进行更新和归一化;其次,在重采样过程中引入自优化的重采样方法来增加粒子多样性;最后,对满足收敛条件的粒子进行加权求和估计出放射源位置与活度参数。仿真实验表明该方法可行有效:无屏蔽环境下具有较高的定位精度;有屏蔽环境下也能找到放射源的大致位置,为放射源的最终定位提供参考。
In order to use the autonomous mobile robot to search for unknown radioactive objects,a method for locating the radioactive source with improved particle filtering was proposed based on the recursive Bayesian estimation model.Firstly,an initial particle set was established,and the weight of the particle was updated and normalized according to the observation value.Secondly,the auto-optimal resampling method was introduced to increase the particle diversity in the resampling process.Finally,the particle carrying out the convergence condition was weighted and summed to estimate the position and activity parameters of the radioactive source.The simulation results show that the method is feasible and effective.The positioning accuracy is high in the unshielded environment,and the approximate position of the radioactive source can also be found in the shielded environment,which provides a reference for the final location of the radioactive source.
作者
刘浩杰
肖宇峰
张华
田星皓
张秤
LIU Haojie;XIAO Yufeng;ZHANG Hua;TIAN Xinghao;ZHANG Cheng(Robot Technology Used for Special Environment Key Laboratory of Sichuan Province,Southwest University of Science and Technology,Mianyang 621000,China;Department of Nuclear Technology Application,China Institute of Atomic Energy,Beijing 102413,China)
出处
《原子能科学技术》
EI
CAS
CSCD
北大核心
2020年第11期2264-2272,共9页
Atomic Energy Science and Technology
基金
核能开发科研项目资助(科工二司[2016]1295)
国家自然科学基金资助项目(61601381)
四川省重点研发计划资助项目(19GJHZ0197)。
关键词
放射源定位
贝叶斯估计
粒子滤波
重采样
radioactive source location
Bayesian estimation
particle filtering
resampling
