新的改进K均值粒子群算法在组合导航的应用  

Application of novel K-means particle swarm optimization algorithm in integrated navigation

作　　者：夏奇[1] 郝顺义[1] 董淼[1] 任洋[1] 

机构地区：[1]空军工程大学航空航天工程学院,西安710038

出　　处：《计算机应用》2014年第5期1397-1399,1412,共4页journal of Computer Applications

基　　金：航空科学基金资助项目(20100818017)

摘　　要：在捷联惯导/卫星导航(SINS/GNSS)紧组合导航系统的非线性非高斯高动态模型中,一般K均值粒子群优化(PSO)算法易出现粒子退化、滤波发散等问题。针对上述问题,提出一种融入权值修正的K均值粒子群滤波方法。通过观测SINS/GNSS紧组合导航系统的精度因子(GDOP),来修正粒子权值,从而修正每个K均值的聚类中心的权重,进而优化粒子;并结合SINS/GNSS紧组合导航系统模型进行了仿真分析。结果表明在非线性非高斯高动态的情况下,该改进算法有效地抑制了滤波发散,提高了精度。For the nonlinear, non-Gaussian and high dynamic model in Strapdown Inertial Navigation System/Global Navigation Satellite System （SINS/GNSS） tightly integrated navigation system, the general K-means Particle Swarm Optimization （PSO） algorithm was ineffective, and the particle impoverishses and diverges greatly. A novel K-means PSO algorithm was proposed. According to the Geometric Dilution Of Precision （GDOP） of the SINS/GNSS tightly integrated navigation system, the weight of particle was updated, and the weight of each K-means was updated. The novel algorithm was applied in SNS/GNSS tightly integrated navigation system. The simulation result shows that the novel algorithm can restrain the divergence effectively and it improves precision.

关 键 词：捷联惯导/卫星导航紧组合 K均值 粒子群优化 权值修正 精度因子 

分 类 号：TP237[自动化与计算机技术—检测技术与自动化装置]