南京理工大学学报（自然科学版）2012，Vol.36Issue(5)：861-868,8.
收敛粒子群全区域自适应粒子滤波算法及其应用
Novel Lanscape Addptive Particle Filter Algorithm Based on Convergent Particle Swarm and Its Application
摘要
Abstract
In view of that the particle filter algorithm based on the particle swarm optimization (PSO-PF)is easy to trap in local optimum and has the complex calculation and slow convergence speed,a novel lanscape adaptive particle filter algorithm based on the convergent particle swarm optimization (LAPSO-PF)is proposed. This algorithm expends the source of the particle information, introduces the inertia weight into updating formula, and limits the particles outside the searching range. The local optimum and iteration times are reduced. The simulation and test are carried out in the single variable non-static growth model, the target tracking model and the fault detection model. The results show that this algorithm reduces the local optimization and improves the velocity and precision.关键词
粒子滤波/收敛粒子群/全局最优值/惯性权重/迭代次数Key words
particle filters/ convergent particle swarm/ global optimum/ inertia weight/ iteration times分类
信息技术与安全科学引用本文复制引用
陈志敏,薄煜明,吴盘龙,朱凯,尹明锋..收敛粒子群全区域自适应粒子滤波算法及其应用[J].南京理工大学学报（自然科学版）,2012,36(5):861-868,8.基金项目
国家自然科学基金(61104196) （61104196）
高等学校博士学科点专项科研基金(20113219110027) （20113219110027）
南京理工大学自主科研专项计划(2010ZYTS051) （2010ZYTS051）
南京理工大学紫金之星基金 （）
