期刊文献+

三段判别域与最小二乘拟合的抗差滤波算法 被引量:1

Robust filtering algorithm based on three discriminant domain and least squares fitting
下载PDF
导出
摘要 组合导航系统卫星信号在传播过程中容易受到干扰导致卫星导航观测值出现故障。对于此问题,提出一种基于最小二乘拟合原理的抗差滤波算法,根据检测量的分布状态将故障分为三段判别域,分别为无故障、偏差和超差的情况。无故障时不做处理,出现偏差时对观测值进行降权处理,对于超差情况,用前几个时刻的观测值组成的拟合函数进行一个时刻的外延,代替当前时刻的故障观测值。仿真结果表明,三段判别域相对于两段判别域多了对偏差情况的处理,提高了导航精度。连续时间内发生超差情况时,相比于使用降权法,基于最小二乘拟合的抗差滤波算法导航精度更高,稳定性更好。 The satellite signal of integrated navigation system is susceptible to interference during the propagation process,which causes the satellite navigation observation value to fail.For this problem,according to the distribution state of the detection quantity,a robust filtering algorithm based on the principle of least squares fitting ia proposed,and the fault is divided into three discriminant domains,which are respectively no fault,deviation and out of tolerance.No processing is performed when there is no fault.When there is a deviation,the observation value is reduced in weight.For out of tolerance,a fitting function composed of the measured values at the previous few moments is used to extend a moment,instead of the fault observation value at the current moment.The simulation results show that the three discriminant domain has more processing for deviation than the two discriminant domain,and the navigation accuracy is improved.When an out of tolerance situation occurs in a continuous time,compared to the use of the reduced weight method,the robust filtering algorithm based on the least square fitting has higher navigation accuracy and better stability.
作者 蔡保杰 邵雷 CAI Baojie;SHAO Lei(Air and Missile Defence College, Air Force Engineering University, Xi’an 710051, China)
出处 《系统工程与电子技术》 EI CSCD 北大核心 2021年第5期1346-1353,共8页 Systems Engineering and Electronics
基金 国家自然科学基金(61873278,61703424)资助课题。
关键词 组合导航 卡尔曼滤波 卡方检验 最小二乘拟合 integrated navigation Kalman filtering chi-square test least square fitting
  • 相关文献

参考文献6

二级参考文献66

  • 1张双成,杨元喜,张勤.一种基于抗差自校正Kalman滤波的GPS导航算法[J].武汉大学学报(信息科学版),2005,30(10):881-884. 被引量:19
  • 2高为广,杨元喜,张双成.基于当前加速度模型的抗差自适应KALMAN滤波[J].测绘学报,2006,35(1):15-18. 被引量:30
  • 3Mook D J, Junkins J L. Minimum model error estimation for poorly modeled dynamic systems[J]. Journal of Guidance, Control, and Dynamics, 1988, 11(3) : 256 - 261.
  • 4LU P. Optimal predictive control of continuous nonlinear systems[J]. Int. J. Control, 1995,62(3) : 633-649.
  • 5Crassidis J L, Markley F L. Predictive filtering for nonlinear systems[J]. Journal of Guidance, Control, and Dynamics, 1997, 20(3): 566-572.
  • 6Gordon N, Salmond D. Novel approach to non-linear and non-Gaussian Bayesian state estimation[J]. Proc of Institute Electric Engineering, 1993, 140(2):107-113.
  • 7Hammersley J M, Morton K W. Poor man's Monte Carlo[J]. J of the Royal Statistical Society B, 1954,16(1) : 23-38,
  • 8Handschin J E. Monte Carlo techniques for prediction and filtering of nonlinear stochastic processes[J]. Automatica, 1970, 6(3) : 555-563.
  • 9Gustafsson F, Ahlqvist S, Forssell U, Persson N. Sensor fusion for accurate computation of yaw rate and absolute velocity[C]// SAE 2001-01-1064. Detroit, 2001.
  • 10Hall P. A Bayesian approach to map-aided vehicle positioning[D]. Master Thesis LiTH-ISY EX-3104, Dept of Elec. Eng. Linkoping University, Linkoping, Sweden, 2001.

共引文献144

同被引文献10

引证文献1

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部