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萤火虫算法智能优化粒子滤波

田梦楚 薄煜明 陈志敏 吴盘龙 赵高鹏

田梦楚, 薄煜明, 陈志敏, 吴盘龙, 赵高鹏. 萤火虫算法智能优化粒子滤波. 自动化学报, 2016, 42(1): 89-97. doi: 10.16383/j.aas.2016.c150221
引用本文: 田梦楚, 薄煜明, 陈志敏, 吴盘龙, 赵高鹏. 萤火虫算法智能优化粒子滤波. 自动化学报, 2016, 42(1): 89-97. doi: 10.16383/j.aas.2016.c150221
TIAN Meng-Chu, BO Yu-Ming, CHEN Zhi-Min, WU Pan-Long, ZHAO Gao-Peng. Firefly Algorithm Intelligence Optimized Particle Filter. ACTA AUTOMATICA SINICA, 2016, 42(1): 89-97. doi: 10.16383/j.aas.2016.c150221
Citation: TIAN Meng-Chu, BO Yu-Ming, CHEN Zhi-Min, WU Pan-Long, ZHAO Gao-Peng. Firefly Algorithm Intelligence Optimized Particle Filter. ACTA AUTOMATICA SINICA, 2016, 42(1): 89-97. doi: 10.16383/j.aas.2016.c150221

萤火虫算法智能优化粒子滤波

doi: 10.16383/j.aas.2016.c150221
基金项目: 

国防重点预研资助项目 40405070102

国家自然科学基金 61501521, U1330133, 61473153, 61403421, 61203266

详细信息
    作者简介:

    田梦楚 南京理工大学自动化学院博士研究生.主要研究方向为目标跟踪和智能优化算法.E-mail:tianmengchu@163.com

    薄煜明 南京理工大学自动化学院教授.主要研究方向为控制理论与应用和智能优化算法.E-mail:byming65@126.com

    吴盘龙 南京理工大学自动化学院副教授.主要研究方向为多传感器信息融合和目标跟踪.E-mail:lxxwpl@hotmail.com

    赵高鹏 南京理工大学自动化学院讲师.主要研究方向为信息融合和智能控制.E-mail:zhaogaopeng@sina.com

    通讯作者:

    陈志敏 中国卫星海上测控部博士后.主要研究方向为目标跟踪,控制理论与应用,智能优化算法.本文通信作者.E-mail:chenzhimin@188.com

Firefly Algorithm Intelligence Optimized Particle Filter

Funds: 

and Key Defense Advanced Research Project of China 40405070102

Supported by National Natural Science Foundation of China 61501521, U1330133, 61473153, 61403421, 61203266

More Information
    Author Bio:

    Ph.D. candidate at the School of Automation, Nanjing University of Science and Technology. Her research interest covers target tracking and intelligent optimization algorithm

    Professor at the School of Automation, Nanjing University of Science and Technology. His research interest covers control theory and control applications, and intelligent optimization algorithm

    Associate professor at the School of Automation, Nanjing University of Science and Technology. His research interest covers multi-sensor information fusion and target tracking

    Lecturer at the School of Automation, Nanjing University of Science and Technology. His research interest covers information fusion and intelligent control

    Corresponding author: CHEN Zhi-Min Postdoctor at China Satellite Maritime Tracking and Controlling Department. His research interest covers target tracking, control theory and control applications, and intelligent optimization algorithm. Corresponding author of this paper
  • 摘要: 针对粒子滤波(Particle filter, PF)重采样导致的粒子贫化以及需要大量粒子才能进行状态估计的问题,本文结合粒子滤波的运行机制,对萤火虫算法的寻优方式进行修正,设计了新的萤火虫位置更新公式和荧光亮度计算公式,并在此基础上提出了萤火虫算法智能优化粒子滤波.该方法引入了萤火虫群体的优胜劣汰机制以及萤火虫个体的吸引和移动的行为,使粒子群智能地向高似然区域移动,提高了粒子群的整体质量.实验表明该方法提高了粒子滤波的预测精度,同时大大降低了状态值预测所需的粒子数量.
  • 图  1  滤波状态估计 (${N}=20$,$Q= 1$)

    Fig.  1  State estimation of filter ($N= 20$,$Q= 1$)

    图  2  滤波误差绝对值 ($N= 20$,$Q= 1$)

    Fig.  2  Absolute value of filter error ($N= 20$,$Q= 1$)

    图  3  滤波状态估计 ($N= 50$,$Q= 1$)

    Fig.  3  State estimation of filter ($N= 50$,$Q= 1$)

    图  4  滤波误差绝对值 ($N= 50$,$Q= 1$)

    Fig.  4  Absolute value of filter error ($N= 50$,$Q= 1$)

    图  5  滤波状态估计 ($N= 100$,$Q=1$)

    Fig.  5  State estimation of filter ($N= 100$,$Q= 1$)

    图  6  滤波误差绝对值 ($N= 100$,$Q= 1$)

    Fig.  6  Absolute value of filter error ($N= 100$,$Q= 1$)

    图  7  $k= 10$ 时粒子状态分布情况

    Fig.  7  Particle distribution when $k= 10$

    图  8  $k= 25$ 时粒子状态分布情况

    Fig.  8  Particle distribution when $k= 25$

    图  9  $k=$ 95 时粒子状态分布情况

    Fig.  9  Particle distribution when $k=$ 95

    表  1  实验结果对比

    Table  1  Comparison of simulation results

    参数PFRMSE PSO-PFFA-PFPF运算时间(s)PSO-PFFA-PF
    $N= 20,~ Q= 1$ 6.5276 4.6309 4.2862 0.0928 0.1259 0.1108
    $N= 50,~ Q= 1$ 5.5987 4.2807 4.10670.1167 0.1492 0.1367
    $N = 100,~ Q = 1$ 4.7243 4.1109 4.0929 0.1245 0.1977 0.1674
    $N = 20,~ Q = 1$0 7.8860 5.3516 5.02350.0947 0.1284 0.1162
    $N = 50,~ Q = 1$0 6.2733 4.8920 4.7043 0.1150 0.1576 0.1425
    $N = 100,~ Q = 1$0 5.3569 4.5583 4.5481 0.1233 0.2031 0.1739
    下载: 导出CSV
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  • 收稿日期:  2015-04-13
  • 录用日期:  2015-09-14
  • 刊出日期:  2016-01-01

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