基于遗传算法改进的粒子滤波重采样模型（英文）  

Improved Resampling Procedure Based on Genetic Algorithm in Particle Filter

作　　者：张民[1] 贾海涛[2] 沈震[2] 

机构地区：[1]电子科技大学图书馆,成都611731 [2]电子科技大学电子科学技术研究院,成都611731

出　　处：《电子科技大学学报》2015年第3期344-349,共6页Journal of University of Electronic Science and Technology of China

基　　金：Support by the National Science Foundation of China(61172117)~~

摘　　要：提出一种基于遗传算法改进的新粒子滤波算法,该算法对于每次迭代计算出的最差粒子并未简单地进行丢弃,而是将这些最差粒子利用生物遗传中的遗传性和变异性将其进行修正。该算法利用最差粒子数据与种群中特殊数据进行交叉变异方法来增强粒子种群中的多样性,从而有利于粒子滤波对机动目标的跟踪;同时保留部分粒子在未来进行唤醒也体现了多样性。该算法更有利于实现粒子滤波在机动目标跟踪的适应性,提高其跟踪效果。Particle filtering is a nonlinear and non-Gaussian dynamical filtering system. It has found widespread applications in detection, navigation, and tracking problems. The strong maneuverability of target tracking brings heavy impact on particle attributes in resampling process of particle filters, such as, particle state, particle weights, and so on. This paper proposes a new particle filter algorithm based on genetic algorithm optimization. This algorithm combines the hereditability and aberrance of the genetic algorithm into the resampling procedure of particle filter to improve the adaptability of maneuvering target tracking.

关 键 词：遗传算法 机动目标跟踪 非线性滤波器 粒子滤波 重采样 

分 类 号：TN953[电子电信—信号与信息处理]