A Novel Gaussian Particle Filter based on Randomized Quasi Monte Carlo for Initial Alignment in SINS

摘要：

The error model of marine strapdown inertial navigation system on the swaying base is nonlinear, while the azimuth angle is large. For the nonlinear error model, a new recursive Gaussian Particle filter based on randomized Quasi Monte Carlo is proposed. The randomized Quasi Monte Carlo methods use the weighted randomized low discrepancy particles to replace the weighted random samples, in order to avoid the possible gaps and clusters that arise from random sampling in Monte Carlo methods, and improve the sampling efficiency and calculation accuracy. The simulation experiment shows that the new approach obtains the better estimation performance in initial alignment of large azimuth misalignment on the swaying base of the marine strapdown inertial navigation system.

关键词： initial alignment randomized Quasi Monte Carlo Gaussian Particle filter strapdown inertial navigation system swaying base

作者: Junhou Wang Chunlei Song Jiabin Chen Zhide Liu Xingtai Yao

作者单位: School of Automation Beijing Institute of Technology Beijing,China Ordnance Test Center Baicheng Ordnance Test Center of China Baicheng,China

会议类型: 国际会议

会议名称: 2010 IEEE信息与自动化国际会议(ICIA 2010)

会议地点: 哈尔滨

会议语种:英文

页码: 1-6

在线出版日期: 2010-06-20（万方平台首次上网日期，不代表论文的发表时间）