摘要
基于粒子群优化的粒子滤波算法精度不高,运算复杂度大,难以在实际工程中应用.为此,文中提出一种新型邻域自适应调整的动态粒子群优化粒子滤波算法.该算法考虑了粒子的邻域信息,利用多样性因子、邻域扩展因子和邻域限制因子共同对粒子的邻域粒子数量进行自适应调整,控制粒子对邻域的影响,减轻局部最优现象,达到收敛速度和寻优能力的最佳平衡.利用UNGM模型、目标跟踪模型以及故障检测模型对算法的性能进行仿真测试,结果表明:该算法与PSO-PF相比提高了精度和运算速度,具有实际工程应用价值.
Particle filter based on particle swarm optimization （PSO-PF） algorithm suffers from low precision and high computation complexity, therefore is difficult to be used in practical applications. This paper proposes a novel dynamic particle filter algorithm based on neighborhood adaptive particle swarm optimization （DPSO- PF）. The method takes the neighborhood information of particles into consideration. Factors of diversity, neighborhood extension, and neighborhood limiting are jointly used to realize self-adaption of neighborhood particle numbers. Thus the influence of particles on the neighborhood is under control, and the local optimiza- tion phenomenon is alleviated. Optimal balance is achieved between convergence speed and search ability. By using the univariate nonstationary growth model （UNGM）, target tracking model and failure detection model, a simulation test of the algorithm is performed. The results show that, compared to PSO-PF, the proposed algorithm improves precision and computation speed, showing its applicability to practical engineering.
出处
《应用科学学报》
CAS
CSCD
北大核心
2013年第3期285-293,共9页
Journal of Applied Sciences
基金
国防重点预研项目基金(No.40405020201)
高等学校博士学科点专项科研基金(No.20113219110027)
国家自然科学基金(No.61104196)
南京理工大学紫金之星基金(No.AB41381)资助
关键词
粒子滤波
粒子群优化
目标跟踪
故障检测
particle filter, particle swarm optimization, target tracking, fault detection
