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作 者:李臣 魏巍[2,3] 刘晓波 LI Chen;WEI Wei;LIU Xiaobo(School of Aeronautical Manufacturing Engineering,Nanchang Hangkong University,Nanchang 330063,China;School of Mechanical Engineering and Automation,Beihang University,Beijing 100083,China;Jiangxi Research Institute,Beihang University,Nanchang 330096,China)
机构地区:[1]南昌航空大学航空制造工程学院,江西南昌330063 [2]北京航空航天大学机械工程及自动化学院,北京100083 [3]北京航空航天大学江西研究院,江西南昌330096
出 处:《机械与电子》2024年第10期22-27,共6页Machinery & Electronics
摘 要:针对传统粒子滤波中可能出现的重采样导致粒子贫化和多样性降低的问题,提出了一种基于交互式多模型的哈里斯鹰优化粒子滤波算法。首先,在粒子滤波中引入哈里斯鹰算法,通过将粒子表示为单个哈里斯鹰来模拟其捕猎过程。其次,利用狼群算法的狩猎机制改进哈里斯鹰的捕猎过程,特别是在全局搜索阶段策略中,使粒子主要聚集在目标高似然区域,同时合理选择性地保留一部分低似然区域的粒子。最后,设计了基于3种运动模型的交互式多模型算法,并将其与哈里斯鹰优化的粒子滤波融合。对不同噪声强度下的机动目标跟踪进行了仿真验证,结果表明,改进的算法能有效解决粒子贫化问题,并在机动目标跟踪中表现出更高的精度和稳定性,相较于其他算法表现更为出色。In order to address the issue of particle depletion and reduced diversity that may arise from resampling in traditional particle filtering,this paper proposes a Harris hawk optimization particle filtering algorithm based on interactive multimodels.Firstly,the Harris hawk algorithm is introduced into particle filtering,simulating the hunting process by representing particles as individual Harris hawks.Secondly,the hunting process of the Harris hawk is improved by utilizing the hunting mechanism of the wolf pack algorithm,particularly in the global search stage strategy,concentrating particles mainly in the high likelihood regions of the target while selectively retaining a portion of particles in low likelihood regions.Lastly,an interactive multimodel algorithm based on three motion models is designed and integrated with the Harris hawk optimization particle filtering.Sinulation verification of maneuvering target tracking under different noise intensities,the results demonstrate that the improved algorithm effectively addresses particle depletion issues and exhibits higher accuracy and stability in maneuvering target tracking,outperforming other algorithms.
关 键 词:粒子滤波 哈里斯鹰优化算法 交互式多模型 粒子贫化
分 类 号:TN713[电子电信—电路与系统] TP18[自动化与计算机技术—控制理论与控制工程]
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