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

doi:10.11772/j.issn.1001-9081.2014.05.1397

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (5): 1397-1399.DOI: 10.11772/j.issn.1001-9081.2014.05.1397

• Artificial intelligence • Previous Articles Next Articles

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

XIA Qi,HAO Shunyi,DONF Miao,REN Yang

College of Aeronautics and Astronautics Engineering, Air Force Engineering University, Xi'an Shaanxi 710038, China

Received:2013-11-04 Revised:2013-12-07 Online:2014-05-01 Published:2014-05-30
Contact: XIA Qi

新的改进K均值粒子群算法在组合导航的应用

夏奇,郝顺义,董淼,任洋

空军工程大学航空航天工程学院，西安 710038

通讯作者: 夏奇
作者简介:夏奇(1991-)，男，河南信阳人，硕士研究生，主要研究方向：惯性导航与组合导航；郝顺义(1972-)，男，山西临猗人，副教授，主要研究方向：惯性导航与组合导航；董淼(1989-)，男，黑龙江齐齐哈尔人，硕士研究生，主要研究方向：惯性导航与组合导航；任洋(1989-)，男，四川阆中人，硕士研究生，主要研究方向：无人飞行器作战系统与技术。
基金资助:
航空科学基金资助项目

Abstract

Abstract:

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.

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。

CLC Number:

TP237

XIA Qi HAO Shunyi DONF Miao REN Yang. Application of novel K-means particle swarm optimization algorithm in integrated navigation[J]. Journal of Computer Applications, 2014, 34(5): 1397-1399.

夏奇郝顺义董淼任洋. 新的改进K均值粒子群算法在组合导航的应用[J]. 计算机应用, 2014, 34(5): 1397-1399.

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

新的改进K均值粒子群算法在组合导航的应用

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