Research on Adaptive Filter in SINS/GPS Integrated Navigation for Helicopter
Received date: 2014-03-13
Online published: 2025-05-26
由于直升机的工作环境复杂,采用常规卡尔曼滤波进行组合导航容易发散。为了提高系统应对突变的能力,提出了一种基于Sage-Husa滤波器和强跟踪滤波器的交互式多模型(IMM)自适应滤波(AF)算法。具体实现是通过判断滤波器的每一维收敛判据,进而选择相应的自适应算法。仿真结果证明了改进算法的有效性和优越性,导航精度得到了显著提升。
关键词： 直升机; SINS/GPS组合导航; 交互式多模型; Sage-Husa 滤波器; 强跟踪滤波器
李治民 , 张春熹 , 王珏 , 晁代宏 . 直升机用 SINS/GPS组合导航自适应滤波研究[J]. 弹箭与制导学报, 2015 , 35(2) : 41 -44,49 . DOI: 10.15892/j.cnki.djzdxb.2015.02.011
Due to complicated working environment of helicopter, Kalman filter in integrated navigation is prone to diverge. In order to improve the system to cope with sudden change, an interactive multiple model adaptive filtering algorithm based on Sage-Husa adaptive filter and strong trackin filter was proposed. The specific implementation was that choosing corresponding adaptive algorithm by judging filter convergence condition in every dimension. Simulation results demonstrate the effectiveness and superiority of the IMM-AF algorithm, which hasimproved navigation accuracy significantly.
| [1] | 栗英杰. 直升机飞行模拟器关键技术研究[D]. 吉林: 吉林大学, 2012. |
| [2] | 张曾锠, 瞿张峰. 直升机周期振动及导弹发射振动随机性分析[J]. 南京航空航天大学学报, 1995, 27(6):726-731. |
| [3] | 甄兴福, 肖剑, 仝云岗. 直升机舱内噪声主动控制系统设计与仿真研究[J]. 直升机技术, 2004(2): 39-41. |
| [4] | 范科. 自适应滤波在组合导航和初始对准中的应用研究[D]. 南京: 南京航空航天大学, 2009. |
| [5] | Sage A H, Husa G W. Adaptive filter with unknown prior statistics [C]//Joint Automatic Control Conference, Boulder, CO, 1969. |
| [6] | 田易, 孙金海, 李金海, 等. 航姿参考系统中一种自适应卡尔曼滤波算法[J]. 西安电子科技大学学报, 2011, 38(6):103-107. |
| [7] | Zhou D H, Frank P M. Strong tracking filtering of nonlin-ear time-varing stochastic systems with colored noise: appli-cation parameter estimation and empirical robustness analy-sis[J]. International Journal of Control, 1996, 65(2):295-307. |
| [8] | 叶斌, 徐毓. 强跟踪滤波器与卡尔曼滤波器对目标跟踪的比较[J]. 空军雷达学院学报, 2002, 16(2):17-20. |
| [9] | 徐肖豪, 高彦杰, 杨国庆. 交互式多模型算法中模型集选择分析研究[J]. 航空学报, 2004, 25(4):352-356. |
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