论文:2012,Vol:30,Issue(1):27-31
引用本文:
高社生, 薛丽, 魏文辉. 渐消自适应Unscented粒子滤波及其在组合导航中的应用[J]. 西北工业大学
Gao Shesheng, Xue Li, Wei Wenhui. Fading Adaptive UPF(Unscented Particle Filtering) Algorithm and Its Application to Integrated Navigation[J]. Northwestern polytechnical university

渐消自适应Unscented粒子滤波及其在组合导航中的应用
高社生, 薛丽, 魏文辉
西北工业大学 自动化学院,陕西 西安 710072
摘要:
提出一种新的渐消自适应Unscented粒子滤波算法,通过Sigma点来获取状态估值和协方差阵,利用渐消因子自适应的调节权值大小,得到一种参数可调节的重要性密度函数。该重要性密度函数考虑了最新量测的影响,更合理地利用有效信息,保证了粒子多样性,使滤波性能明显改善,能更好地解决非线性非高斯系统模型的滤波问题。将提出的算法应用于SINS/SAR组合导航系统中,与扩展Kalman滤波和渐消自适应扩展Kalman滤波比较,仿真结果表明,提出的滤波算法能提高导航解算的精度,其性能明显优于扩展Kalman滤波和渐消自适应扩展Kalman滤波。
关键词:    Unscented粒子滤波    渐消滤波    渐消自适应Unscented粒子滤波    组合导航   
Fading Adaptive UPF(Unscented Particle Filtering) Algorithm and Its Application to Integrated Navigation
Gao Shesheng, Xue Li, Wei Wenhui
Department of Automatic Control,Northwestern Polytechnical University,Xi'an 710072,China
Abstract:
We present a fading adaptive UPF algorithm by adopting the concept of unscented transform and the fa-ding factor in particle filtering.This algorithm makes dissemination of information more reasonable and overcomesthe limitations of the general particle filtering by using sigma point to obtain state estimation and covariance,andthen the fading factor can adaptively regulate the weight function.Thus it provides a reliable importance densityfunction and is suitable for filtering calculations based on nonlinear and non-Gaussian models,through consideringthe latest measurement information and ensuring the particle diversity.The proposed algorithm is applied to SINS/SAR integrated navigation system.Simulation results,presented in Figs.1, 2 and 3,and their analysis demonstratepreliminarily that the fading adaptive UPF algorithm outperforms the extended Kalman filtering and fading adaptiveextended Kalman filtering ones in terms of accuracy,thus improving the precision in navigation system.
Key words:    adaptive filter    algorithms    analysis    calculations    computer software    errors    functions    improve-ment    inertial navigation systems    Kalman filtering    measurements    models    Monte Carlo methods    nonlinear systems    reliability    sampling    simulation    state estimation    synthetic aperture radar    veloc-ity    fading adaptive UPF(unscented particle filtering)    integrated navigation   
收稿日期: 2011-04-01     修回日期:
DOI:
基金项目: 航空科学基金(20080818004);陕西省自然科学基金(SJ08F04)资助
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作者简介: 高社生(1956-),西北工业大学教授、博士生导师,主要从事导航制导与控制、控制理论与控制工程等研究。
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