马天才, 陈淑静. 基于修补粒子群算法的红外目标跟踪[J]. 应用光学, 2010, 31(5): 843-846.
引用本文: 马天才, 陈淑静. 基于修补粒子群算法的红外目标跟踪[J]. 应用光学, 2010, 31(5): 843-846.
MA Tian-cai, CHEN Shu-jing. Infrared target tracking based on repair particle swarm optimization[J]. Journal of Applied Optics, 2010, 31(5): 843-846.
Citation: MA Tian-cai, CHEN Shu-jing. Infrared target tracking based on repair particle swarm optimization[J]. Journal of Applied Optics, 2010, 31(5): 843-846.

基于修补粒子群算法的红外目标跟踪

Infrared target tracking based on repair particle swarm optimization

  • 摘要: 为了避免粒子群算法退化和运算量大问题,提出利用修补粒子群算法对红外目标进行跟踪。该算法先用设置粒子的惯性因子对搜索到的红外目标位置进行修正,使粒子的位置达到局部最优点和全局最优点;然后通过粒子群收缩因子限制在边界搜索,消除目标位置的模糊性。利用该方法对空中红外战斗机图像跟踪仿真,结果显示在500次粒子迭代,100次跟踪中误差为2.83%,在最大惯性权值为1.2和最小惯性权值为0.3时跟踪效果最接近真实目标,且边缘最清晰。

     

    Abstract: To avoid the particle swarm algorithm degradation and complicated computation, a repair particle swarm algorithm for infrared target tracking is introduced. Firstly, the algorithm uses the inertia factor of the defined particles to correct the location of the infrared target detected, so that the location of particles reaches local optimum and global optimum. Secondly, PSO constriction factor is limited to border search to eliminate the ambiguity of the target location. Results show that there is an error of 2.83% in 500 granule iterations and 100 tracks. The tracking result is closest to real target with sharp edge when the maximum inertia weight is 1.2 and the minimum inertia weight is 0.3.

     

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