[1]闾斯瑶,周武能,李龙龙.基于 SAGBA 优化粒子滤波的目标跟踪[J].计算机技术与发展,2020,30(03):36-39.[doi:10. 3969 / j. issn. 1673-629X. 2020. 03. 007]
　LYU Si-yao,ZHOU Wu-neng,LI Long-long.Target Tracking Based on SAGBA Optimized Particle Filter[J].Computer Technology and Development,2020,30(03):36-39.[doi:10. 3969 / j. issn. 1673-629X. 2020. 03. 007]
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基于 SAGBA 优化粒子滤波的目标跟踪()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
30
期数:
2020年03期
页码:
36-39
栏目:
智能、算法、系统工程
出版日期:
2020-03-10

文章信息/Info

Title:
Target Tracking Based on SAGBA Optimized Particle Filter
文章编号:
1673-629X(2020)03-0036-04
作者:
闾斯瑶; 周武能; 李龙龙
东华大学 信息科学与技术学院,上海 201620
Author(s):
LYU Si-yao; ZHOU Wu-neng; LI Long-long
School of Information Science and Technology,Donghua University,Shanghai 201620,China
关键词:
粒子滤波; 粒子贫化; 蝙蝠算法; 模拟退火; 高斯扰动; 目标跟踪
Keywords:
particle filter; particle depletion; bat algorithm; simulated annealing; Gaussian perturbation; target tracking
分类号:
TP183
DOI:
10. 3969 / j. issn. 1673-629X. 2020. 03. 007
摘要:
在目标跟踪领域,粒子滤波技术有处理非线性非高斯问题的优势,但是标准粒子滤波在利用重采样方法解决退化 现象时,会产生粒子贫化现象,导致滤波精度不稳定。 针对这个问题,利用融合了模拟退火高斯扰动的蝙蝠算法对粒子滤 波进行优化改进。 该算法将粒子表征为蝙蝠个体,蝙蝠种群通过调节蝙蝠个体的频率、响度和脉冲发射率,伴随当前最优 蝙蝠个体在目标图像区域进行搜索,并且可以对全局搜索和局部搜索进行动态决策,从而提高蝙蝠个体整体的质量与合 理的分布;融合的模拟退火高斯扰动策略可以增强算法跳出局部最优的能力。 为了验证该算法的优化性能,将该算法和 标准粒子滤波算法进行性能分析对比。 实验结果表明该算法的滤波性能优于标准粒子滤波算法。
Abstract:
In the field of target tracking,particle filter technology has the advantage of dealing with nonlinear non-Gaussian problems. However,when using standard particle filter to solve the degradation phenomenon,the particle depletion phenomenon will occur,resulting in unstable filtering accuracy.? ? ? In response to this problem,we adopt a bat algorithm that combines simulated annealing Gaussian perturbation to optimize and improve particle filtering. This algorithm characterizes particles as bat individuals. By adjusting the frequency, loudness and pulse emissivity of bat individuals,the? ? bat population searches with the current optimal bat individuals in the target image area,and can dynamically determineglobal search and local search,so as to improve the overall quality and reasonable distribution of bat individuals. The fusion of simulated annealing Gaussian perturbation strategy can enhance the ability of the algorithm to jump out of local optimum. In order to verify the optimization performance of the proposed algorithm,it is compared with the standard particle filter algorithm in performance. Experiment shows that the filtering performance of this algorithm? ? is better than the standard particle filtering algorithm.

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更新日期/Last Update: 2020-03-10