基于DE蝙蝠算法优化粒子滤波的目标跟踪
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国家自然科学基金(61573095)


Target Tracking Based on DE Bat Algorithm for Particle Filter Optimization
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    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,会产生粒子贫化问题,导致滤波精度不稳定.针对这种问题,本文算法采用了差分进化蝙蝠算法对粒子滤波进行改进.本文算法将粒子表征为蝙蝠个体,蝙蝠种群通过调节频率、响度、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,并且可以动态决策是采用全局搜索还是进行局部搜索,从而提高粒子整体的质量和合理的分布;引进的差分进化策略可以增强蝙蝠个体跳出局部最优的能力.为了验证本文算法的优化性能,将本文算法和标准粒子滤波算法进行性能分析对比.实验结果表明本文算法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 the standard particle filter solves the degradation phenomenon by using the resampling method, the particle depletion problem will occur, resulting in unstable filter precision. To solve this problem, the algorithm uses the differential evolution bat algorithm to improve the particle filter. In this study, the particle is characterized as a bat individual. The bat population adjusts the frequency, loudness, and pulse emissivity, and the current optimal bat individual searches in the target image area, and can dynamically decide whether to use global search or local search to improve the particle. The overall quality and reasonable distribution; the introduction of differential evolution strategies can enhance the ability of bat individuals to jump out of local optimum. In order to verify the optimization performance of the proposed algorithm, the performances of the proposed algorithm and the standard particle filter algorithm are compared. Experimental results show that the filter performance of the proposed algorithm is better than the standard particle filter algorithm.

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李龙龙,周武能,闾斯瑶.基于DE蝙蝠算法优化粒子滤波的目标跟踪.计算机系统应用,2019,28(2):24-32

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历史
  • 收稿日期:2018-07-12
  • 最后修改日期:2018-08-09
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  • 在线发布日期: 2019-01-28
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