摘要
基于传统粒子滤波的运动目标跟踪方法中存在重要密度函数选择困难、缺乏通用性、重采样设计难度大、粒子退化现象难以有效解决等问题。因此提出了一种改进的粒子滤波运动目标跟踪方法,该方法采用人工鱼群算法改进重要密度函数,通过粒子间的不断交互及协调行为,使其状态接近后验分布,从而提高重要密度函数的通用性。在此基础上,结合人工免疫算法的免疫算子改进重采样,平衡粒子群的收敛性和多样性,抑制早熟现象。实验结果表明,与传统粒子滤波算法相比,该方法通过参数调节,提高了运动目标跟踪的准确性和抗干扰能力,并能有效地抑制粒子退化现象。
In the target tracking method based on traditional particle filter, the importance density function is difficult to select and lack of versatility, and the re-sampling method is difficult to design to solve the particle degradation phenome- non effectively. Therefore, a moving target tracking method based on improved particle filter, using artificial fish swarm algorithm,was proposed to improve the importance density function. Particles interact and coordinate their behavior constantly, making the state of particles close to the posterior distribution, and improve the versatility of the importance density function. On this basis, in order to improve re-sampling method and suppress premature phenomenon, the parti- cle swarm convergence and diversity are balanced by the immune operators of artificial immune algorithm. Experimental results show that compared with traditional particle filter algorithm, moving target tracking accuracy and anti-interfe- rence ability are improved and the particle degradation phenomenon is suppressed effectively by adjusting the parame- ters of the present algorithm.
出处
《计算机科学》
CSCD
北大核心
2014年第2期232-235,252,共5页
Computer Science
关键词
粒子滤波
重要密度函数
重采样
人工鱼群
人工免疫
运动目标跟踪
Particle filter, Importance density function, Re-sampling method, Artificial fish swarm algorithm, Artificial immune algorithm, Moving target tracking
